From 4718b7f973e4e4faa696bbd32ab0fff6db98dd66 Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Fri, 26 Aug 2022 17:05:03 +0200
Subject: [PATCH] added video link

---
 .../_build/.doctrees/environment.pickle       |  Bin 170462 -> 170476 bytes
 .../_build/.doctrees/schedule.doctree         |  Bin 64257 -> 64544 bytes
 .../_build/html/_sources/schedule.md          |    2 +-
 doc/LectureNotes/_build/html/schedule.html    |    2 +-
 doc/LectureNotes/_build/html/searchindex.js   |    2 +-
 doc/LectureNotes/schedule.md                  |    2 +-
 .../Results/FigureFiles/Masses2016OLS.png     |  Bin 11846 -> 12483 bytes
 .../Results/FigureFiles/Masses2016Trees.png   |  Bin 22155 -> 18884 bytes
 doc/pub/week34/ipynb/week34.ipynb             | 1741 +++++++----------
 9 files changed, 705 insertions(+), 1044 deletions(-)

diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle
index 2b4b93826fa5f4e9cf790bced0fe4b8c4cab2d6c..30be57ccd0af43202eed4547bf87120556e6c404 100644
GIT binary patch
literal 170476
zcmd?S37A~RRWEK^vZT>E+9j{oYFirF>KU!x9cQ%K9&ORcb`md>JKcAtyVaX}(P->A
zU<k1t8UjfNUP8#ik_V4$EO`k85&}Hn5eP330tq1yUU)149)EU7AphT~bMLLXw{Fke
zDoxMh!~Q;NX8P7Wea`uvI(6#Qsj6?j`mI-9ape{CUuQ$JUMN;C4SUs##YU}K@v5zE
zXYg>lSk50)K6*=c_jBE6yF;BNZoS?;-C3Eh<ysBT%NHBn&O;*)H@!x4q*5q()uvY+
zIq$hSP@Nt*C4PD*J-1P%zwy?{NiWxGH@wreme(Aan51tS8TQFFyPajtMov(@%bF^P
z<Z9Ka;&ivO#uqR6d)X0w5Xv)9yXZ9<#k|)P;;ksV)#<i7?Lo%1_1S6CM{}atnk{?8
zn$T6Zv$D{tlqXsRuYyn4^QRM&wc3T?vkkJrM6KQ`)~ZZ?T@#wf6(%O##zf1z1XXVw
z7oRw-Lb2%(iQ(blZf9-TZC9yXyVVNb-B5+b#FrGSEw3@<=J4gqb7i;LEao8TWU-p}
z8in@cM7vS$b~Y7St$OqBk&((QKN+qyppdntc6}DFhwZNDcCLmblV115&OokKcKQ9n
zj&^5t-E9>nTD1wF)9e;@bk;V!daYS()f%(>B@YK*lB-qf^};MTX%0^pTcAE%tfhvZ
zv^7HPUmB)gp;P6HCL1nP0qs}ZCK!jxrfaq7vNz#Y-STV;-#Ags6L+hj(1trvbuU7_
z-OhbyYcpQM%R7^^&b_WvXn0fi?FjhwwICD@z3h0|L#!6(Fd(>h#C=&eAU9Qm+8acF
zuu!Xb6LlEJM6n9B<hn0(Hn*GJgvzV{4658j570eY`~m}s&)3uE#b&eZHGOouFSa{d
zguOIt?MBWk7poU0n(e8n;wAdlWz}|tO!<Y*wz^yO%FPKkpC@B$H6d+%BG+sZ*=D<5
zZ+Ok-#0+<}X7>q*P?>GE(6=gX5eDc6c3W(bHQzc~ua}F^&r!GKI-{h_CT!5H<{iJ=
zIzhjM^4)f;P=i)CovJljUe$S^gr9}%L!!fX$#UwmjpB3xCcSk3-u?UVk&vleo33>`
zE9l2?y$S;y^e*Mf?K~;61;v7|C*W2#-FoYAu|8X!oX8ct+=Y4#HV$VnFnn};VjT7c
zeh1DM+Lg)XaDEc(u;1u<o$CNPsJQ5kwcJENy8;cv-#{^|1s7B9#UfmQuoCgXq}%kU
zn?S+qV7uJSeJ4R~Ze?M%UPHHlMnGgGQ1!?R;Ux$y!m!dVx6l)E6ToNEYtXm7(Ah!?
zpYW>gB<zzot2Ra7ClObnbJeO7Qy$2`h2(4HvfHSjhoKh}=EbR5Al7V8HbJ*dJ_CAd
zxy=jZVzUKF2L){~J<)`YDqw}EqE{5lEvR@YnKt^euy8cr;$8A`g3N?lF4txz;O1%#
zVJq?p*|ArtL&QO-zSeHl+bwuI_z^(8v{|UlpfAeitzyMPqwd%Zoi%<Bn#jS$d!)zh
z!6)3_n+W_Ba9j%W0gu}yDIY1t2s*3FE_@=iH(jhkLo4d}De*~{dzsQ@C;0Ohh&Kg&
za#!8XVe4^YgluSd77nQ0xw`4K+G3=O!MM?P_pRDAoRHtI!$E+$LGamf0az90x;jt|
z`5OXRj3YEy!0mR{`F{p>2oL9fqDt58Y~fFb@#(M_0{HK6kVB6m|IjUL>1^`}=sNQX
zhYGhBHk>YOENm)lE^H}mEo>|7FYG8Bc%g9c#loG1y9##~?kU_`xUcZC!u^Gp7e))m
zUo4zJg9q^cgN26*r(P(WE}SVmTzI7L>K6--y;yj>@I>Lsu^S2#Xz-eXTbL~53i;0R
zOCF<Cn0lcwT_|)ms~VeVdS%1`<WW8~%n$ZB!*0G(s}{-hhx3hEJztxt;?tHpJyF02
z4X+FHbn?ZE!|;-2{!@9Lo#(l4-;w~^yGnsSnWM!7eY5y_^|kOr^cLSq=i$dIMHnZn
z|0;4{D<)`+EW-1?1%EvGc=nkCd!D_yJLbHhd#+n}Eq(N){?TpfN6&XVtGJK&p~os8
zUmCRV)ul;(O8u5hnL3_u9-Xyb9dQAMiis(3AjXEZ!VgA1S|dIxDUH_fkQ59@!iN!V
z!l`vuooT>Rc=@BnTnkd|J_moabhrU;)fvQC)M~Uvq?hMNXUT9S4_8N>B1@fO89V%L
z;ce~C8Xn{(rfTF7MI^So$=?H4InKX1w?Lp}7z+_Sg3$5e^sxBo>K3_MDD^!36d$e3
z)$(3;vRunSFDp;Nfx<u2leXeA5XiudyKtIY6Z+e*TQA~U#?j~$+Jph))f^4cAaON%
zVWZY9ytlLBXf4+kv9oAbc>ihmW_-T<d@Xk#VKToy#2>CW4h$ZI-xZcX&OF}+|KHYq
zu3PvBTs{TQWH<f?1EfB39Q+9eg3(FXDI!MC$suBBdfgWu`Ud`c@x>Qf-O0jFB2pLM
z^pVbBv*or(ctmAlbPZ^(#(?9Nr<D)A&f#<3#iBRk)TW&A@HJ-!!JKo>Yq}K#3((<t
zFIT7*pF;Q|B88K1)o#NXEl+!h(|kr=&=^^pE4X!ZhJDI6Le}8p{mCC6NdEX>^yBS5
zZHlui#it=K+($&(;A;*kAA6l!k2h)+h&D}O6W(!JHD?@A6yh4^5#gJHIH)<VT(>Kf
z6}j$EKK44<qh78kzKKktxyLz%$x?+(_LPU<D$g24Ul!In?|0s*5XW~8BC4YJ7$7MR
zyw0|x7_>zg;+Bi8SqPj*w}ia&e!*P|9elyM0K>gKK97MLwvVA3ey+mnbv7UMym}aM
z1}cd#={?__?5qeoOyPsD^wKI!WYOEiqp<7nqjmC;(uw%lM)^$dCER4IY!dA+{P<e=
zI54n41Ki+7y+NP~ZjjL%1UlgcyYvQu@pFUQWP>@n0k<8IZ3BLT4#5o$=nVq9<_34@
z4FU`22KVR<0xRVP_sa&ly>gQyvPr;ZV2Iq{glrI+QecwYVl1IW6q+*$Euz?rC$xxy
z^Jv%t(c(k{6S8jML-?)YwOou~oz-Hj4TiMN^3r5hgn#YH&Ptxj(n!)RyuY)KW&tJl
z(gB1BB5i@Um&>$4XRzL=Vd~L*0CVFbG~e%b_F_=jk5?xAN3ZosME-s)hcgpy!*jdM
zW@jCT2l!SoR*4A&zJ4VV0tNU?kn9%j>M_GBU~F+a%PzWQxPBg>@zo{xNsP&71%Ym7
z39?At)6aJ)ge=ubKL}aY`|TiHFh#^{0n-)4+0+)HW~t@3U5;c7c}UDB!uOXq5GrE2
zrhayHtCn|X)lUY*ClgbZR`+!9D#G00R|HPAoQp{rQcEKIS<ZiBRvb3j#-Rl!7n}kT
z_*GrX@oag9@O^gpnc&&8&tOn~wi~|dOnaCjUTXPst*Q3YPtQ(ZeqL=t=MaeIUCaG8
zBz5UEkht*v@ck7;-?xY0!@_qL-WvRlNrFfYV0MJih9;Yxfp%*uduO&;oDSKba6Nou
zb>5qDF-e7KBD#>8*hcw-DWsQ(FU%PlNO57J3sheS-yNhjl^Vq*$fwX;Q$D-C)hJeA
z*0mN*@R0nNLJWvG?L@OqDXAAjBCACl%83KWAAC+Tj}Y(;1>_9Mls5>~4%_!R59R8t
zm(66q2ncr8(j1m-yGcoff|wUS8J`7Kiq#^*%W3bx2&@#z%31svDHkV4*!&L+-!(!J
zVHQ$cK#r$5;s=G)X*sWl`C;%y-Og(+{|oA~d8A&TH_E{3U*UyXS{Hop#3WLcl$;D7
zt+{|<Qzm_+mNCr>6|@CViu4-IK`_OFm+&aP^h7tLvl3zZgopV_ZCbGv@hSRm^wSN(
z0^mlV@zw+;>C>uxtgXX7Tx1A2?}X`3eO4m5x{9PRGOC<OJ$;%y#2|l$?i1uop6!YU
zt1E7m@@-9#ZImXw0+YmgF?V6M`=V&K*8h8=I9;V&(G&dors&5o)oHjmeu(*QsGg1f
z>;p3n{5FyI>M)|QD}(2S?<>3`_?>;VpYx&4F73Lro_z3x_Y`y+czwz8D)ErNNa;)~
zTS(}^=~41WGPH_5MPkKkAPFXEtVG_aF#&xbM=w8J)r7WNPqon*RTwrXA5XX(@pYCw
zb<~qsY*L%}2^se71A(6tzVfP)3*Wy&r0Qv6LV2epfCke`@_Aja9ja%Im}pXVO*qa{
zjT}2+yjna6-1~`_uMo=*S9F%r=j0JPD@6;Q*_QqS?}>lt$p-mE$)<;o{TD@6Q+#3g
zWR-Z)PhDb2BC14?xeR24g?iO)_>kwy;0jY`%)`3Md;T<9j$`+QZ><wwg)Bw2Rh%kP
zntFM=dZCI8hNR5waQ3cS`Zra?72@?a650HDFnF?TIa;LX?hdugpH8E>r+n%&-tfXn
ze)s}&hul57tDgV}o)x~m&<UkrO$S-jcvD|*$Sn9>WJy4z!=lj>B+aBDv!W>#fx#b^
z_fC>Ikd2vwADV!_Kqpta(k7vf3gR2_Xkw<Ar!FbqWm&}lgN!KU1&US7Pw^E2#@s?=
z+i76|Oa)H{1#ztS<Qhe>HWR~wpCc55n?o+I)`&e{FW#SNAW>DFrcn|B!b&8^i%;Vf
zw;ZxS>9x?mt`oTxVco$X6+EL9T=+W@aKZ0ba6pVS0U;Z$3FL$^iYpI!4T@K$!r_2&
zcERhEv<rX#j>30_zrU&QUBU0cSI~Sv7w|RIdE)ZNwqQ_GvZN!(EYtUNJC7|GeVK2S
z)L(J=)cwq@r1{9@(+o61Nnc6$RSQNqNFPhu=i{`E*Ja57M_?CX-N{E!2}^{1Vg$gd
zn;&?EhP7Ed!(^p3(XJ!R@ep&0*gOdJIoa5R3&Uz1jXJSc5rK+KFjjU%2%xsuD2Q`>
zfi!TwNDDJBgar68g|KxG`9$~y<jy*q7=Ynm81t7JH<DG-at&7T8WXiiF{Ke%ypSIE
zU4@qXDy*`ii^@k6VwtIP)i3;aDGz5_RZkS?!0+J5thGoCc&Qh|FXd-s-xD1AD+2Pj
z3h&3yNJROZ^7$bhRt9}LJTMYYwEj#Jrzj8P>VqtA=uLd@K*aTDOjqzACw5XXq3@ar
zVm<kc*W}2-QuIKX)b)|I0ffVtRjxTwMu<mQIr<XJ!DL^jS#G5cr%3N(GI_23sMzFi
zkJh$sQXbc;<ykQk4QSA)0psaxR~j|E3PxPN1wB;1fZ9!99?+Vd2;SYPyi1?aJRXBX
zTTK11qUkmefbk;o^-5Dj7nqP@2{BwOAYnEsEqL^6UYw=}8<hug`D8t<hlJB32a$bL
zRh&{66Ta>%--NI$nEE*w0NuKW1n99#^&w>IF`Dgmo>}SOzXle+8dZls97fM5BN!bT
zd2IOB+wOnz=4YNA+Wp$c9)IHT$DbIP-s9|g{PCN2?%I8iGcw}bywj<8nDvV6^h^OW
zPX~c7rMP`!E1zAt677y4be_aS%b6))G1)~TyFTmi!kL3b=o~@s<%gX|i{-L2={a>|
zJwStU-c<~^_{D8GGj7<DXu?d|WznYUOgG#~(V9#|WU753oO;oNx1cPycq9n%o7G({
z`YLplyi=??oIF_>s*4qQ1dN^s%^`aG2$^uTwMVee+@BSeNiFt35GdEXRCn%krrdJV
z+vAY+5e05_l1vW|u~33%Y;^oYeoBftM#dIxOjbtU9vImX*cd47*s;eUt2$cC9i;^*
z@ezS4P`!vYVh&WlC;VHWa}Pg<4v*HH87wQ%8oUF69r_+fC%*{Kd~4=(^^OOQojaBd
zQ%XDT4=F>B#4~XTHg38piDk9%1I)K*tI81amO~n=<@pkjUx4{iB@t|%yn&B0)jGhE
z1?Pf%ampDAkO=IXZQRGEibW5<7wmd+XqR(aBAobm*Y2U=TX*l;EyzCWV6D~j`^!A^
zB0Y@|joZp@jXFyW%{N=VC6Y49E*^VAqQ{aSueDLM;TxHJI9+Qg00;|=Jd_Gop7A#l
zRwrsLHb&(;$s~qklb{z7#^?vbO&ZCDc0FF*wOiG+urW{}s>9-sAvQzSBq@(T;%2XX
zt)nu|fBLMdMCq-l_)K${k`hDoRgyZjjfgZ{Z#N4=%Ind##7(q2?%b(<S)31|&+g(R
zfyC5>IFIdrB1&2_8+w=i)85xbcvcu5H+Ldh@^$M~^UX)GU?;>UP*nG@+u2FJjX0!7
z5n3cLeIM!yL~%}YNbQ0Cn*j)sH;EhIC2i>@`B&J`7n;81rUVxlSLZycP^iVcBTy1j
zP=0D?w{zcp_c^<EnNk;AW0WGv;1M63!iXctHhD3W#vryFxJr1RDbf4zY1JuV1&(y=
zU^0jz7Prjr2~vTI!)`U)L^;{eMd!YkIrs;x(l8L|H_8x$-lJ-zg!(A%XWXMDyXQpc
zf)N!#c@fH(cHrJfDdaHSi?w3jp<0}5qg@qlQjEVCU1=^G@~M0=3PXLH+VOaG#~kdb
z?=&%ddqaEoIN5z{{cO2@RH3v)G|+8_p(uy7ET@v_zFH)@WjxKrl~hfEs0uMy6G<`|
zT=K+&Q+cS%3If;8Rl|-X;Rn!Mj2Y&EWZ>%uxBOhmI0r#9v)~tVBDc{H!JSBS`K5fc
z*pT<L7f`<%ouiN@6!|(af1qjwQLG>$%4#jBI4xaw74jv~FNIqvAhe}gPkgCN1B#&f
z-~^>5@VT5vAcrazmw1NOxhJTA%;l@Yr6xt57aPM>uQgJwS4RA{FGr5Nd1wScWh<3(
zHb+dNvD9C%Vx^|z0yftMU+8CKC|h{d<3q@@J-)lEOdf+~`67l5Eaq^#$Ee*CO1qF8
zh8?PdlpqC6?5A)$@~Greo@tW)3AkDpEYSFN1B-b9&nX)?7(AjSAzm2|pREp_wJA6E
zV)*%n;B)Fselem<dtDPgm6O<(i?s(>Dst{Y0&Hv0OiYu5)uI}5hue=oeDb7odi2yW
z=iXzdMjt%+^3kJ5&m9{de_1zVVY7VOU$4UQcLnp@lcT3kJUn^=ubw>l@^X>te&uV0
zeBmP5X=$3YQy>+P4p#Egps3dLlk)rt%DrK0VuI~w)vx6TaC2OS$CZ#MZfsYoem>q`
zXrv~WFt0^wTYSlOezR4pm%WP~GFx2N1J8qeJ#n##8^kB};+aoVOgK5^cU85<dH#+3
zns`LH0wmy(OcZaSkQ!DzffYni;)%&7>IxA@QrSiKmH!3e3#UiE{uU~GLG1@-20;r4
z3sG>LVmiRtgRD|fmld>~TS5xX<B-^2K6>mVOsTr(1S&l)Jn>AMqGnO5?KR~nt~7t9
zT}9>qHA~bhWQ$uIq({k#i}arew=nu3pBjGW<4AL~YL8PWOv{WYo}{$(GmpbVcr%m|
zLOt&@kE4T7fYzj>{Ik!{@K1j|E5GP*)Vm_JPc@IvgtSR3D8dYu9PcAr-xGZb2SVwe
z@Iy)Q7L>Q-FGHE#rT}mY1E^$!f~+(>E<9gg)ACJ#^Q+Qf3lf7&f}i<$D%ChJc@faS
zIQ;hT*J273TTi-{963J_CADzysD3LpV8)cd@@dL8D4F(Gn>yR&s|}B;r&<#Q&xH>U
zUtF)YKmdcJ#MqUBr1B&vz~%0uJea99E)cP?tK+Xw@jTW2vYxidZ}A^U89P@!c~jRF
z?_VdIHPC@DETNVWIcP5bJ@N9!&x_?7(RKhP=BMc3!ckAKj2Bd~0>X6x4wO*1#~r(}
z<x^inZ^(|!J@p#;u;N}o8j9TlE!E>U>^1QF@<uA@E_-6c4D*Jiqv{a39-X~dof111
zA~g;HPhx!tD>}=UVDHO~XAl-aQV;z+Tnu27^(!0JoGLb{xDyG`TD#c|1lSOKjqV)>
zgGY+X5o+2JJV^9zA>&dlofW|vJ6qP~fIt=Ep~j?GhD&Svx}o<`xy_m*XM=?2@>}oA
zzHY~DuaN`J8qvxR2+?9Gyv`;0c%yhsdDAd_qR1*3u80Bg9azQGBCDX$%1e|FMWokK
z7c|%9ut#G81^;421*WZxf*WYz0q&~D<3XzGrwsE2Zx&Xb=jF%c$m&j4U6NA#R-1w}
zSgFU}k>xbPmyt{D3{d(EVK^+Pvw{|eDXB%}(!AV3DWwJyjjLPO8{+0CL`4W9k3f{Q
zIlr0%;!IpTpe#>q%ZU~D39O_Cj=*mtwY4=@uX9pYUvv+?VZCV6OEH`1>D;P814NGP
zY4BdVfR(ow3p;2W-AHfp17T0JQ!9A3osxR>36zagUCN$dFH-ITs#KZ<MZlZ53s2Ap
z-mX5?hK{kQnV)E3l<*5e!b*eAX7mDdORro<AXLr!B_mzY)j9*=L$S!aG5j-Vi?Hf+
z=@h!72ms{cymyf@xER#1sllse(fz_^+hjBU8-s=A&XxOQv$e9BNXsXF&xJ|K^HNP@
z=&-Vq#GoXBhO}cOkq9LV;3lKbx5{_9_#$w{FNT6_mrYQgf_!$PGEv4Dj)6mdkD{|C
zVkhndrGKROEzq07Cf7A}qtDmL@8N~S&MMzGKLFUIxYx>d6pfIPpyMJ|K;@{yk)r>w
z8JB^eUk5yGk+g*Pn2YfSjddgKW@7}K(nbnJY*4|1Y4D1ov5+pmvp#$$cp%?$<QHKs
z5pKQlHwD$OIxH1F2(w~Kjg%-*$&vV680mT%Vxp}&1KcAJD<BmwOf_tjymlg{n1Tq6
zrNJ~*nY}!PfP}hCgpqK*GUz`R)mNghO1`?<e<mUt^p0u`d*0;aY?JypVTM+iDO?J^
zr*wga&$2RxmMZu|&QMEL{29(BOEvrz&b3SR$X`!I{%YVag-kR0XDj+=8-JEA(m%m`
zZL>_1`m<<Z2$CXgHwj!dZ8-^l#}<_EcWg@ue}8r8v9M_+_*C89#IABOaFpw5Ga3>)
zH7a(aZ3$wLGH~5%@RVoaG%%t5fd7z#=?2*V1yM){p$B1+GkBl&Tlw$T%WaHOvEqnL
z^0^pxF`!{u-x(0wC$PX$3`x=cn+Vy8hnXf;HEEg?DXI{w+B7G?K3}(S0mD}ru~qnP
zugz<PGhvghf<&GsDgM}q{i3wzNRE3<MQy@$fwF~8B#&$v)GM28Q@$r(Ln#hz{-H=s
zVRxfw!;8xZ97U2tA+bq3_CKHS=U&~?6uHd+1SSzUjpi)MH$+if@Lb<=S9m0RnZJOd
zc!Z_cjLKoQOvtbzg6B<u%!_cc#ah9lh-dwx;gC2hAP4}tl2j~*D-v!Hud5pdBNocS
z;jvs^<k;!TXS$s$pH%{#&4Q2^S7<4F0z*-|<#oq?HDCu@xWZpgg}+kZgj$9m`$Fru
ztK9O8JKID!;v!puTK#aB*Xm)>c#qgSg>A^e6X1$XT__z!07YBj{1^PaTHVg(Va^9)
zyOr4V#ed19A#F(uoCa+(3;8@Q4SS=qTaGsT)G6bj^u?{}bFrV1Cp?%<`SFz6AeaT6
z=4aQd&wNv&dEperN@}2s?R5cjovqQWyy&a`PF`sooBSrULkkOcMH<&4_^u0gnqA}~
zq>y}Jw(y4VtFBYNY6=s7cgCZ{v%iHhd<WY%{oP}P9bE1-at4U1D47@{UU;$a#*lFM
zLXdmFmNN=h(UC(b+9-Bk!`V4e{DUBF>`Ge5rhJ|M35WNzz`YDvA=>tPy0aW18P}Gt
z^{Y0*50;<xAB8)zf)fJ@15`2HS%EQ{YnI;xi(Ce;ft^m60AN-`Tafm{iIPX9vR+Id
zf{*W%zaGGl3Q)1LD|~uT{(1+JWpb;H{2V);16qeD0_Sg|R79{TNjA70zlPgbuy@+u
z9EC3qf8VS8PMfKNHef&an!CV>n97I^UiduR{_<T=dss?O{c_OPBF{aW!@2I*Rlz3F
zV1M{9zM_gUJP-SOuMD=t$%jRnG$9zrv*65#kQ$DJ7+V?^*aRPQMGM+*k9~W|z3B3M
zo7nI-yN2IHE}K5z9D9ocu<qCr?fYrRAzp~Hg?-HUxX>KCvao6F&UR;$KS>~W4GZM*
zB6z6{k<YQJ08fN!eP0YX3i4wuS|FkRH`G}xa=w_)!evnvb>}L~VNl?TNwJ^t$Eb^a
z?&5DzpocnAX!XU;<{UcL1r(29@=n|MY3CU_^$R#@0Y%&t3G%TDPLQOZnVA`8xhab~
zf*^GSEt?~7p!5d5AIVPC-cz4h-I&TLG_bP3=RdSCD{c-Kz1EbVLBkEbPpu>GuN9tH
zuDg`pgrTyt7QIPK873}K6h!KxECYs>=%?$0tPMQ)6fI+1K+}~47yA!HvKPLzvqeP8
zIEnyn1*U|rBBz#z{2SFu<X~sU6aI#h!jjH>dk#8lf<+u4i+W5k`@ueIs*IipN~7DI
z6_~A4!42aW_Nl$y6QjTP$(`@p^H&qHqwV7lKlb$>dbj*=KY#eEA85Zxet3XC{9oVu
z;m?MB({K2hBQs;+XMVfi{f!TOH2mx!zx(-n5AO><JLG@%cQ3v!{OorA><gbe@b}?o
zckpMw?>zT`@UuJp&))UrFNL4o#h-m~*?V4{?3aY}U;Mx~e|N;QiBG?@|CvL9H{=fv
zF8PA$K*fiDxUqXgaX<9gt@m6ra5(h7^vOd9H~rwZhfJ}0-}CsP8}EsDF>3jq$A9r>
zyQS-vUU3Ef`h`1>{O10%IE(}}$F%BI%~5K!@aE1cf0qarCusLE*QcYG!l^GIC-xR>
zcBLK3A<X-_Z&DoT0s2e2QvBgNef*|6c=esV>O1?@KMtvX+)jTajsPk#@xC$?;aR}W
zohr41>K_rV?+{NyuMYyjXop|bp+O-{N<c^O*ZEOy?m^9^0{$Uwb=F`}k#b=i#|ior
zn$kWMzv&d3c9!}|7aA^|!AIm62PlNZmSj}#(Vtgyrs7m-Gj@H8kP@T+wQZ%ZQmVGw
z`OGsrXuRHWx3hzN9g3KC_&%9F#Lk@IeS7J{3KGy1R?(+4hL2pz$~UOvSBiAVg$Rex
zM$*N0VA|pqC4AsnkPOfG5fdyT?6*BYjjNM>6oep!HU@hQY9WaUhXzJ?-wHj0yWa6E
z!qc;?lhQl=pYO)cQ(IP--h-cG>s}&Cctw&Desc{gVZ%#A3FnfOuzm$AVdYCi3FAph
z`0%x?gv?7s3FniP@P=zx2{>;{1~LDM9pl4EO8EO_tb~D=h!P%2QbJ`VD`Cw`L<x^3
zDdBf~C9HjkDB+b!N*EqsOIZC9QNpW|lyJ>UELeJVk`e~}U}^nJ#1bA$Qo{9{xjVd6
z<_?c1DZyRBN?7_5v4kgt5~A~`Us}pKSk`Wqo=kZDkt_N6Rd_xjp7)rOj(v|ZCA|`@
zU&AWz(frigS19dQy+jaj=hn$18AT`AmxxX#Stq4G_kVsBKTrM6>e650=h)y&L<y9c
zLsq;;x~I5Qu?zInc4wJL6^o>+$Q6mCGF5&whiNk(5_VUM7D(Cor%Vqt+!=q@E7paO
z7Q-RYNOV)#*uUb-J4??a=|V}ro#Lcwq~@{X;W$nfagNs-H~}~BoW}94*ne@_Yat7D
zL7aPsoK)!>^mf{pP{}Ax$HD%8yw;w?0lG>F8>P6G6h%Fne+)AuLC*SWUVOrDD{vTs
z{DbPOkZ>1m2Eqb6yfKCah0b!Ea!GANU9=}f4yp8Yd^KxDFpr{#E3Urc3MgU;esuPa
z*UA@3{G-Jwu@=xOLh56v*b?Qy4sThB9IY1Bg$;qy0KU3(4gP^nN-Ob`RJ$5K!PZ*-
zF~~nQ@Q+RWV~hAPw$5#rw&B;(cKic5DNQtwoYx=0Y9va`Nh$%WiY_0F7{WVGTjqYZ
z_-Y&O2V>k*erF!;Z###hcpEU_7IxzZ&avTw?6P^ca|W4C+LKKn()o)~c#Pg;$?mk4
zY<)~JO4`gL*)`|rJR&{{67`fd+DPf2rx+Dvu7=F_32VM_x^TpvD9<tv-y4)8El=_3
zi&1`)8Ze)aSo3*xj8961%){r#leKAafM-v|CbnQsJ!?*J+;>Eyl&_eF)7?GJ&cvo?
z#lqbpN|NYc)3CQ1yvI_ttfg8JlZp}$^GM}{XLeEzEzazmTbTKqvkc#AEyF-e2Ff4I
zBSY>OR$SF{M(=jS(WkC64yS`1`g9yAZ4W%<R%gW@ytVrn%C50(hK|G++XMGFr?8AI
zcGW-B7B6C}Ok(F*ly78BzS&xnD`T3ZB*Z+LJapK_4F?V?VsY{SogjeCIA?KgLatcH
zpmYRv=d~(1`S39Pp|eLucSr5rk_Jor9&2e=#iXTd!#vWi^JA!BtcuE^J!K9*WX<84
z7>AS!n1{pBGfH{Q9sx%V`lrB;3XupRGU@nr1`Y~>u$YI9(v_%83qX&x@Cj=zERSh{
zasl&b;pQXZ$-iPCez=OuAiT!KA{}HAUf~dNy6IkFSw3wo%j%dcG^L+M7L!XUA`+Rr
z$a4LGv0TwHkrEK|NHajw6fQK2FsppdynOXCd7)&-JiKfYhd*JDn6Qx(4N<llHUH!$
z%;CR8IkcVmunL3u9%6q=yv)P-JttB4D-Rjp<2-;pji@l1rv5$JXrSO!$W*}&ALp!C
z=*L`8Y;K9g8ka?h^{{4HWY%M=hfZ_`c$ME%6va^XWgf+>_Z8DC=1zFdLS*_P#1dTy
zN|($d#J$J-y}5)YYC9+0Nt|1NgUZhrJna1w$c|I(AyqlTu}5yJiQ4bwkz&={6;(}X
z5B|oic9?(c<sbX`$3gyayZAA7W#H}Z#NV)N%96|@`;h39WXVA_D9_iad=5t_)#&Ub
zoDGUI1tabw{c9}K1A?J&u4`+vzWe(c<Z4g0v1Po5jY@PZq0H=vL<#3DC_TV4dzxyO
z#4MSnrX?|}q{&`M%+qK(RT48Hny!?@OoKxFl8DYJP%eoWmjb<#h|wqrDv9`of{l`h
z6KKFKiLsJ~tCARL$ia&=af#fkNC=n6jj_+6j$9I*jCw;!SSo?fxCxcvcvHVtP^7Im
z^|WfTT`qfk?nIcdilt^mj%t8TrkE)5Jr7A4oFSCEi(_zc?@8WIgsrE|i*&37;ujZ(
zSk()2e@9rs*{hkz5~WResa956L8=m&o~NSrlOoeY1!k~Zq-s#yiLEhH?FKfiP@Nc3
zXkw>?$aNu^MJb`uR{Z0S3<FXk{_?W%m;00wRLK;l1jW*8@FNkHlGpReg&t2z^<EEz
z{;xSo=Lk?grx`;I(<vu7RxH}H=W58zzZqX-mUT;sMd#X#IPCyjX2E5axXgxJ3Y?zk
z4vnoHyAsF!itOEUV1=1_lQC01i5Ef=9YMkZl#(STJC9wWJ59X19qdHJ7Gaz@hXfY3
zx$+qxbi@yelVcNvv@fw*e#%%aX|dXw@tFH8GoOtzpADE-VM_w;$BjAEfct<YiH_nQ
zSzSNICeW+**#f-@frK+&n`+@W;U3*Cxe0Ui1!MWrg19+D{jABHC3m&4tMtzHyT%M@
zn0?lk#Bbw86)evn`_Ei-BI761;=XTAt0dWZQAo7#o_q8tom5k!9b;Hwq7q|qEMgw#
zZ_&wGkz78<ZsJr+^gkR#!y9JQLAA30^jK%Bm-lNNIE-;1kU8sO9KfkG*!qDp9%*P`
znel_yFS2w$RF-5WpA*Wg_?-j`9x&!zv*2Zd#3BwC*B!xqjoA1i<|EknL*mk%ERHCR
zk6=7^Q-*KYSOsal%Jmr>#YCKDF-r!QA*Wd`;^#Fu8py9o8WC<YW=z9o$d-ic&bKhU
z3q2HUJ&!~2aNH~oCZba!1$Njf63Y_wY{k%x8;h3~x@{SxE{jzG9PuX*6a1<DDQQeN
zYs`~|+9_KSeMjwj5k2D|VR)jl08gx!wqWCQ6JVt8&C`|<yksnDS_E(47#hcT5w8Ns
z2(J+wm*``A+n6&A$A&G5zT>#;q&rJzR7C&_K4fIR-<bKd$Slv`j6wSL2n0)Y2;OeY
zf(F6&+LGuy1na3rhDLKyBSVL3N6|=l&cOVFu}Eow*^rr1c*5;V;3<O9)jCF>Fy=<X
z=x1$7^c|xeM+#VQjim{M+w(V*V@H0Z3#EC1QT$V5q0^$6$>8`4a2-t>2nQ&+j=;K1
zhxIGQ>}jxm*_K4#VZF{@tE7p;JXxT*n=+KEagu+3@<7*Te8ec+pEDD5)!1?!!~tW*
zG!U2BlIS~#Yq8YQ#<lJKHM7xv7=Oq>?K2i8ZJ%40!TD$^H<A9eLWgG9m=z70+iXen
z9hxntTpVn7$#E*U^RS46=>4;NBd)SnBZle`W8u<5wIzd<9B`M7F&9B>Ku2ucm?I6b
zv$iDSh*{)kz7eL2c)o5yLUJ)ripMrRxcR{frt+eO7DW?u#swBAVzY`KZP;p_H&$?Z
zt0@M&WmVo3t4Wwcyxy1}&1RmnCDC`Zb{(S|Xz3ado((E$!R0D)Rv-1uso!Em-)Ag-
zT11C3p3lEOLsa<B)~eZX)~|-Z36qo`HRe#m_`S9y`i}9I(Y&}wK^#d<>!3LFL7O-B
zYQzYB##p$t2yV@|g1c;-JEc=5%ve5c%#a4yr)){|9k4Zz6q|HJ?9&Me@7Obj=O2wl
zNej=~49eaVUKCUkCLDif%!h``-`JAqJ1W;UbHxkARu-3M<0>zVBwS~cfYF>S8L?{z
z`Ze9SE~E09jq$1AmoV4ZX3Uj_-)379eaA1>b0TqU>=|S9a$`}__Nt|sVjkxz%cX;a
z$;Exf->zYCw=IdjW3e~fW)@%b!*arToc>d5OqFXhB1<!d{XDU>n)&ODsAsH@w21D@
zh-)yDR`jp{LFXB<L?VNOn*|_|0)N6xWzv{c4gLvR5`Bk%CzTK!#=%%vGde=kgN7(W
z+=H8;W_Z;%0ra#lG1PA~7CtT1J4Knnq~JcA6Woi1r7W1|<y#HkmHJHdd1L-Gd|z)%
zqVM?LdK?wT*%od?&{p-Gc$^pU!zM2>&L1>ZLRy?}%g|jlz7(-_`njUjRXW)3GiFi)
z`$ug_^d0P7j~0r#f`cN<;zbb*g+)QZ4WbXy9d7r>rjTjhVwgW`EPh&;cV%XX|E%Oc
zEFH??1asVgnl0k81e}2>hwIfk#Gf%{P=ol>wj}xv@iy|;=Z>9xSVBAHR*E?7Jkoo6
zH)9b0#aO_!AZ`~vx#5-D5<H(n1-3OhZ2xG?k_Ow~*^=lxY#R^LRl&|_%E2YiCKFpQ
zSU0Tf*9>J-W|H{Ac&YNG;Ivl9=~`oMG@Q2ClIS~5%a5Ye3kR`BdsFx+<8j28{j{BD
zMJDfmzk<O!9fOw}bD&{xpDl^LW3XQA;G)f@xF(%Xw2%0<gy)P)!C0iUxNOK^!$Q6Y
zo=}eL^Q0g&s3YVV^P(X%X-lH-2<;xl4xT#7CTFS+(!9+&>XnKYaW`x=IGiLXqKx(<
zgZCKG?=V(DT10QnxY<AkT7d{;pj36N5L68}*6T>W(U?aK>E~@p^d0F#C+T`T5lf0~
z;mY<~k$j_B1=Y#onmMnIs$njjjiPQzgDv1Ej8&7?0&dTUtN&QGY;7tlW#hymWe`LS
zFv%8{1*_JuLAQnv8uP1J!~1MW#I3<%O;Rpv@>U`X5|Rtowichb2o4GAwU=q(3*v3g
z3sMKB(oNyEvcEKjFxhJ9sO`jUWkcb%vXb1Z^ykLks@dNE5hNC|`23o1uhKCbLrzCb
z&?!%OFNbTBRpoZ(YRIl))vARFlQpGxt_k-f(OKy}lUZgm3<K6B)ts*|=1GI(YFiSr
zBVHv)^z1RV^#$9{c~n$@Ivu(}M3jD^-ay}4Hb*-~Zm+S3DZ2}h!=_8gqbT|ccvO%}
z*vXMK=1D{DR$CH%M{Y0-vFUnPbh>C?(;qWV4;u@V7N_+xzz#T4;7KS2IcLm@2G1E=
z5`Bl~I!f|{JF=SoId~{obj}<-7KLi=rVQKD#)77W?Rt^y3HMR?oQhtCT>>&xS~r$Z
z=`mx>mIh(VmPBd@hiskWs|Z6sAm$Jm1BW9nB638%{vL<s9OCj>hp!!`V<HO0JT8La
z7C4iO_Vdttd^a3_95sMGeh*vEyNq?5)_PtktjE_8huV-eD|D0<I?9GB%Cd?mPG=Qy
zgC^Hu<1N+@f2tZ@LdD8Ej3v?x?`?ua&p2tphguo^oUPTxfQz~IbfY$d3&nbbNqR%Z
z^OMHXr^Qo-N%fG)tk0z4&96=hls<0Em4?a3Y)MFG@KIY5a^A$2aar@GK80&HKTNmG
z(<WcBMdnz$jKyA@#6n&hhvX%VF6mxjyZT#W-KDjwTQY6pJlNcm^@ww1Z?M4}oF|Po
z2?bIA*O)!c!2Z&fgfy^uptT-t_KYh*0u2~>+$V*rbj6XfXKO{AUs`oM|LokT$1r=9
zvEI76UsJ7YCK5!zu`7~f>nuq&ltghW2}M+!jM>yMUoS{3o}<5sj#|Vnj3BS(Qfz>O
zlIVOWI!Ot~C`bH#iuMfIUB<dd+dX$+Tei<L58^Hj*35I(=UK%m$^GAM%$<hR0b3HX
zckZ<%A>BXQOF!MewU4;rEDnyO>`L;1+EKfjuQ3}{$yi}&jq1h>dI52Z{x6UJ4m-Y5
zv$bw4p=58`m@&<s@`6Oqo<FqUG2XiKxc`sl06~Hx($Tf2j6la&(6k5)X6Qab3b+G)
zluo=NsT03w%!{V<H`tPpo%mZBw}n1nVbf_|+;AEge2R2>Fx}@Ifhf5N<MiXkf~Cc2
zb0(NUML3Iqm5|^1fH6ZFSU+Y<A`X^?yObW6`>6#9$wj}SvKzzXHYEedks*-n8Hml>
znl0&%jkTWEl5Pys+?Wgqzbn`covu2qPN<XpLu1A?i~6D<(bMNHxXZm`+!ME7JMFrd
z00sFPG2d`T+pQX{``<$ccB0;@jI);;^hxaKG;1mh=+ZU)3a0PK;D{B<&1c(nF-;0Q
z1<$AGvTu@4JR<SP(+lww=P{7v>8~_qS%doDZLLLi+ppP@kj{e*uAk1sHh(y*&gf=b
z%+P~L4`&lkM{a?MjYOs|vmqWbMm4Qx8Hvp5NSe^8@RXrv$;G;{go^F`#*Av_H)2ad
zn%_L5hAr@iaDPXw-M|T4R8ONuzo7;2q0pk_BB;WMMk1Cp*bH86teUiDa64AxeA%eZ
zMwKnJ2hQk<l8aC_#T_M-bU$j$uVxMBZArwf!NQYBXT*!|79=DW|J$|%fVc?mD_Xpg
zi+S!dQlsnA-mto&Ym8R1fzstniPaT%Bvn^@xAC`XX7-(eM9=VM;ir^aW21y~vFRK+
zeL6N;^lZic;3tg*PpR(CHcVaUoXN1bDtob+%~h+)?0zsQ{P>VDM;a9Gw<RI_+J9p-
z7kZ+(ozD`ZjT$)1ujYu|Yze5%(T;KZJ!28m;&zRH;uvklK=fG43LZ7&5@wyhW6YC=
z+;7>ENR6CzhHWDbYC(z?<uEx>Eva|4#1;(KzZuJw7OqWnL`%RIC3NClETO*QUyZrZ
zfchsvViCRFh;oyj<4?VD4c*yVdA3@sQ06msMvXeYbd29-*lt|AFtaypFzSpTuO8kE
znDtM^Zek525U1ynJA-3OlHBHX#;j_XZnq^NJKq*TV!@~Kuj&Cl-(iKTz>rwQJ#Jp3
zUTYTVu0>H>aTd!IwLC7qI$jeI_t0^K-bJj7;yBf6lPbrWu}iM{_&sb;M~zjS)(<?|
z!=M5cg+<VTibQb*R3=nJ%deP};D^vq76-ce8j^V-GQ0$@KWZ$JW_T~NB@s6~i||@T
z=<L!gNJuXF6g6x;KT}heN}V&4CG5(GM|!<lvFWvqm7Lb}aQ<(UziiFtFPz&ACe?^F
zj5*RQr)Eo{@0e{nMb|pP6CIU{^>9tNbS5$|^=`(fz1>*Aw5V;*(8W_cRS(Mvm_r4&
zgbJ(gHD*bJt!qo7@37r)yxl-Jg@Y1Q*k*B!X~o68w^8usYs?US!dTd}5Z;)<1vSL2
z>Kt$fRTe8)f?Ea1gsPdJHD*i$@~3S{qz2MDULU6b9W|LaaSa_Y@3q`wc@Iyxr~pF=
zzYsg}R5d8+6tB&HJW5}_fo<k1#(GU_GvgVWAfOHtC%K{fvtD?PM{5ZYwZu2e(OYw$
zu`-x+wCb0Qh0*NnOM=8APB*rm$Ewh&(+`fH4^G>6a4|-)8g&3YTQQ2u*7a-pfFT#F
zc$F%y>IzQV&oX~XmtUSVPgr8ilLp2Wwj^Yy|2M{Sq0=jy#%G(jxQyoLSjx}0v2(Q+
zgDklT<8_;{U}<~Z=1eo>4Bk25ECN<SY&~SmkOtN*wj}xv)@^4So~UZTl+D9bKK)H4
zMsG5-XN}d67TVh~xU59V0S}ty8WT9mv4qUbDPuM@z#p<D5eM92CL||Q?656JNG|#`
znG%;~9eMEh=()Z7gl~7SjvjUE$xYaho-<Z&T0`P{wXkW#ZS390M_mzTijPRBg?pVb
zJDR~fV@o0ql?B?;V0ih&f`sJa+ilaD{`#1>>$BJTSnu^4>j?F>b6&39XrR_C9)I5L
zoVkd@>l(QNg{l{e-b}<mOZSJ%WgjyJFWDSP_Qd6~<4Mb9A2t3~&FVfZNGxJRH#q7v
z#VMVRpKXn(w4*=f{{97HQBx|ovmU2ZFh@cDSN8SwNuk^C8ndEl{j;_tWH0*+1Gmtj
z+qO~cVYr06BU0ev*oI`7dN*UZzF{n2TDZ1n+$w-lI81^qA&C3BF-sb3|6)s`@37s6
zAPy;Zu}A#0H-q&U+@RZZ9;IVLMk&#bv;FeOo&=6Ax@H~if|p5)4dCX%g_(0{4zcqx
z3iNzgF*BZ_4J(i}*cJi9AXyRJ`?AJn&uZsRi2QCc=3BFp8*EAR-Aabeh@2YLz>Brh
zUiJ{nucd#Dv47B50ckt`?o3VO*{Ev1NlPicP*$oN)+gn>j~lb7!F<G)L>y*|U`xhp
zJbqh{kX-!4+#`u~T($m$S4H&HqEo9*(S>#~vr2f*_Vcu{X4Bda&KL`M;wuzFLmAuP
z`Vh;*US^CL(X6B;NGxJ}b)%y7(LBP}CU&|Zwd!xJh+!~qYewW9#)?Rbh@&FnN`Av~
zCF!p>Bn4w{Gv-W#=zDBQ$S(92M!8ooX5EGMKH9)i5au3d+70J;v4I1m=Y}^rlAFKI
zaDUQRBWdB@mziOSaF#$LJ4F&@l`ffJ04n|oQQgOlS=I3W7=zbq#<}2C6l*!3O~+20
z7>(Bn#vd{u|Jzu~w1BM3aP}J9WT#PV5K(thhx;RARy2kGfh`Hy;l5x?LXMma`(=%s
z-0dw$NG^DMupl9I!Q;0D2|wR5j+{f!0jj6<Tl=(Y&K2|1o~R5uiQ77Bn3YVUWSur`
zMZ8z(beLV)y?$XP6C`P3cIDxu?8;8#Z`CY4BS`dAZtrB4^~MHS9f>jj!76S(!fc5T
zm!#^fGmU()>NaMblj1glahz5F-ADA^Ib=tJJ=FuoN=x=s8dROzl?qFgGh@!D$VPL4
znlBrjEQw*I>B|--ZkHyxvt!2mY9JrBB_X@;{cI<_;s;x65PRzASP#19(V?ruXa$~5
z67cKuqQKAJoH4E#3!E0?o5Xr*z;RY^OlJTp94l}pgaajG)-*V$ZAtVU&drD2CRS9b
z(3tu%X2Q<+BS)hc_GrVvz1di_w7_k_A?M&NOT1BEq-F8u#k`CrmL@H;y~&s#4XutX
ziN2$?{s`v0O*#rF8L5QljM7JpMM{g(1{9})C#o_{LMY+<xSulSMMLPvZAtVUp`C<~
z00`Yk1SAhuX~Il}%jjPi25}}#d~{6v5@Y#AW8u?cxl3RvphQO#Rm8=Bd+LGciOjv?
zFcYc>KX1&S2Jq)>N%S4yU4Ssylc9J_0X3~b`Ir30;-A!SF~nDH=u>do8B!u1k?r)o
zDub=^uV9=|VEFI00KE`i!22~@5`D+`P*5p~ef21#r_x?$2;kNlEf*`g<bIKA0OW3Y
zwuxt0)$$s#{RWma82_Dp!~bAdS1M(r8xC1C(e){d5{QO#86lcF<R%=~l`-a4>+g<2
z;;l=sxbh16@7eB9X*Fh1PjpLb`Ntst*uXzFi61_#z8l3gr#!DEhTS8zi<JMwrEBcZ
zFGa(pxf(LOV~VutgiPzfugz4zr5K2FA(!m8FR3Udl$$(Y%#?=WF<TOS2XE8KV)X(g
zJ;kL5iBWEH69%kqELd8=Hsdw}!5ObLCd9Z2C$?0K+0mdX*^=lxRB3ZLow*t^Y;QFd
zGA(Se>qnO+op$kN<8RkcdXp`QzN0i6bSUaejWFBhkBP-5cQn^&Ql6-ZqFtnpqUn{W
z(_8?0jQP(Pt1K<%hr_N)y;REV50=HSTtJaImQqq(aKecgA2H@#vzVW<CDC__*>e;d
z5-*B{r<3E(RHIgLTybQcJ6SAaXA6!@X<~XY+13lY&0b|Y_(Nkwq_u-=Chtp<t(7tH
zg(O*59Jc36f-O6WBuX5eaOvR}joH*7|GX`UzC%88*q!y7fBemFqbwk{qyTX&P^HY(
zce3K`U1{HC*q3hX*UTx4bQGka*os6E6Q%|&JBZ)PwCtcb1+}LqT$*^LF`F9f|Nb8q
z_B(^IL4#jJUup5bJwis>!F)v65Oy1DIBox@3#d3`iy8#+l}%?6YxN7Lb{g}mSwzN`
zMBEWruKTg#d40-)gyh1p%?$XfkLMS6^>G7E{&FdGg89WMuT`k!quHO*6PT#7a&pEH
zC0iv)n7Hbg$5;7=A#09aWBjd}Z9O4KEaIx;YAiH1YQb)*$j0jEBUbFU7>k%vv7I#;
z#I2H(a5zuA4&j@O+0ZooT3Zsb|I9L23%&HW1v{mSRRlk@_kBDF?4!2c>D7o~`me^q
zrG;rL&ZuMVXy1Dnf-A)*S0^2D@k7Q8X@I@QmPBg6tm`6F&s0h9Or4|#jMT3iOO+O>
z*xdQ*q*c8CY5eUPEWa#B^z>Lgi%@JA#s?19YAwY5Zaq4)RX%6U{<X1eX*IhtGa2%r
z_^HH0g}!Rcg{I*@vn3%7|4(d5$Z99X`Lg1TKKaXQFzaz9i*6G|2Y$X7=8^YmQ&UkB
znX4h&%#E8CW?G}g4V`O6c|?RuIwFX<RE#KLA@@3CrZf}UZc9R%P@ljXJkqE&3Au7H
zFE%YjA=4i-P)CggO53^DXL50l6nGMrVMmQw(cpQREs4Ivv*i>W{ed_Hu{Uyy6nX<D
zHwsd(MhsQaSh%!MZ4J-gK_MJ-CpP`cq`=i<32UlT#tdnI<!njx9kAU3Fsix|OXS!&
z7CB`9L2<`bg|>7=F*JCOVf{{H6{LlAH^ZvP5Ns0^3vNYm*%gbR^vr|>({DFsQ3Lt~
zTN0^(whlVC9J}N-az$Dgr*z-hMlIn)vtErD!=E&kE-i*zaaV%Roygl^6`Z+K5KCAb
z{g5$38e;DkBznf93tzW;Z-4`~oF|p&Izwkpj_)QPij<~v9LEA)pxY9v2`8&u9x2B3
zcZ^k-7Sp>!Oq1(%MVcOyLWqfMJ&BnO#Z+NWs^$GHW3Dyef76zP?5e-cwzJR^`g^2>
z9M;2BRD4Z|9*jXONN=^gT%lTgsvWOjyDUm<AOC8srnL5PkF<}3IG4oaWPT_nPQnVs
zj2K8lb?`qKv#lA(-`kStyMdgP2I9oyVaIJ^;WxoDaQkf@I|8QpbT%<sdY7=k7ai9Z
zmNc8tjhp*5OL|C}P&^9HVtyy}pqNgU)xd|bDQbvVRKj-g>x`w)ENZ(giN0IZVQEpk
z4kUN#^caa8E+vpEj@r>e5M=8)YOJ)h4ro+b53dg?#RJLX5?2E$GDnOiVJ<#u%)Dka
zFS8}Fm_`%d-gVhVV@;5arf94*3!{lIE>ng(A()xlXc966Q^w3|Mw7E8(RZW4wQL;d
z-|dWwLR}Okp#s0*p%O_?70lb3;r>ozanr(0m$)&%2u7%`81+QrFbcX#lQJ6-50H>O
z_;zE~G%#PVB@qYHA{#3cx;#;`AR)QnoS+2>$ptTXT9A-jy!vvI+ql%{;l*9X-gvS$
zElTxz?w&1&>)4+)wv^<4Kq4ltV>gr5u|H${t(u8{T98=8S@^tK?IgBfck_3~0;g1S
zC$<Q;ENSlhH^$$tsr#>NNyuLG7mV0K&wZDPjgPU#>)=Dy{<bX(Go8?kytACQHY$tL
z%aRtSHybmc0kFZAMBf3}Osiw%z~zJqnMkEak2Va?ea51t?HpUgnpnVFLb7OC(wg<%
z#{6h#-C;|j?`WM3?&YBvs$9I_m5YU1Eg$9!n|$vT&SVeN7Oe`;JJmK0ZldGZaifHG
z-2TFoXB(L`R%luqc?IG%%8K!sVNxTZwKVybEu5<!XhbOw6$vNSNW!T^%aV>4n=lqd
zGq1;ON%Y;kHa}Wy;(U6npg2cpV-+rG&{p$$v|*s1Hx?}|&|5My0dLuy;4K!9FB?mk
zTD{(w9}TVNY)Qn?vIt^j9Lxce1qsQ;r)+}&;sSB-Ba0gc@5Xggv>(!W1Q+pOtLG_~
z&g%7=dz_kba<5ygM1zOYGhuZ6Nn_-ajgbUTjE=u0DLVeR@waLw_c1}jB09ESEZp`0
zj$Lfj%Ptfw78_jL*4-+&v17Y?H{<UAN5-<J)OcrmrXV>KZW|mT`vMNb8TpE&==l$f
zS<>M6f-M}jHEi1`CX3zp<mB9@7tOi$ZpLU_wRK?vXU+bB2IakC9KtPwLluoAJic-H
zczoTKg!F~~!jLZXY-ataGwHRkRoijvSki{aiXxWqoT1v)H>ft?3~c5p8}byvC}A<}
zW@B!&K68^Ti8w|UL!KNpx#wDtkX-z3e=OzJQRk_4F?Rt;9$CzN(w)Y-_%!ykJEz*^
zRuMHiII|>bLMAV=ZN1VMinLDoHrSSs0wJ|5`Ywl3gg0!G6pGO$nB2q0Oll@~&Xz>q
z!A7@l)}~rcj#dzgQvfxJX~J`c?pb4z(n3dWfq2R)JShkztcN~r%!`K5j4g@2BeZ?Y
zYgBQJv50-DSl2-*qlaqY_;UFi?HI8iFcvW_V%KCYFpoTGjqs=-m#`@LE@PfF<lbRR
zqVLEJ9W72x;dUS#2;=KVV0a3PI9>yLUYk@_MLR{KU2f-GukDgAF{-~}EPh&4cV+Sc
z|5+8^ir_yR?sHY$XTm)9my8+IApWE+iM~U;p2Fcu4S9*=8!Hl?Gi-lpEK*w7Hi!UN
z@DvoEst6^l3;ww=FB(Gs$CgCj5z5NbxR6_{)mz0%@oBze1bIrj@zA>@bCl7ZX!tH8
zyM9~0rZ&UM$y`|TRb`=tV3tK@7gf{~N{rVU^Qob}%9cbNb&D`qM$(*;uplA1_^9o)
z&CfqQy14l#=ctG3X*w{l)$ry|JH0VXJB=9wmh5#%-o&)icPFKt9x(n^&C-qu5*BGE
z+dRi0FaFV5hAXrj^$hxB?))WVQBx`y7e<M*PDROwi~U!glFt}O%4SR(v!ZD|Z%ab<
zESG^>=xoLyUr11Pj(cvajVibpJlbQ1=S{`}rH6+v6ewq>0*=DScOYrywqwkS2G5JO
zBvQj;owU97aJyI*CBEEy_9$z8QK;r@$*BF5v7Bj9yEZfF^NI6$s81;iaB}z@NLtJN
zabu=5{5~K^ETU6ebIKJ*+$DLm*fZAr=Zu9+tNFEvWr!DF?-6Aud9&X(=0sEX|FR_^
zyUTAdVhiofZWzz`2gWLeRkSTChs%N+vDEr}jTyPG84H^hxf?UhfLo=v(CJn_w~F6e
zlH~dR*_bsA%73sW5r@(uYLtN|_g@PVl8bqUM{EDPdDJ=LXY_JB**Q-UL$q`zxeeRT
zp6v@Wi_s#*&K9Kig3KQCrbROeiK5-c{AfnAQ;=9hcYE_A#bz5jkDsQkE=cv@Mnuos
z69XoSNxD}UqSMAIN!!J5&Rmq7i#l>KHbzlXU3TIn7h}f!X^=f&OG5UrV~qMjyX*{K
zYB=N0ikfjnUjk{;S1cw?^#&u`G!{EOvV4^x@l81ch3^n&Wj$nIY-y4QuN(8HAzQH}
z5l7a-{YnqcF2RC?<f305d}y>hT|<_?P~qDg#<6%yexzNOvw!j@F)K^|8k^b=8RL-F
z)OKgwPy)U%fF;Pb>vXw_$X^K0AF;QD1oC@~S=6lUor1(7y8c@zFMMS5+<7Ndf;bdD
zIw(hdf68|lw_i3^N?P1@iu`fTZM3o>?*hNx?mzLMvtKmkP=oE~ZAr-P`E!i?Lc9N)
zdf+SfDn_wQ(VlVrs<Fsvaoy1iSGm<t9nureLi;mg?lesQ#Fj+gG2M0qM~~oaVLw<q
zQLGkmAa&l09p2x&86!A&O}`@N?YMl!=THpR)8H`694d3J#M9E&7&D~-x6+nG-+{Yt
zoUT>zoU^kySrg^KbiYokM*H~k08rEki-lK|5#nNw%A5iCWzu33IApA<w4L~6nWm66
zTNkoo%_omyH#%0>&BGK#-LY7N_hpUEw4xn6VVbqym~+ieMr=vM?ZhH1laU>d_!cB2
z7yXLtu0KYnh1O?<P13aR!J`Lcm=GN|=4s33Hf5~wwB~k02E_}+tFS5Xnl0uJ$gn^)
zr-XIooH1XTF}+5RSj52X9%XZZ<4(KqAJy6nA21NEx{0`3z$a!?iAVZ&E{hTa`+~6o
z(*k>MM%k#46>?@FC+`oBM&|;0o;3ysges#=;<mtVGv-?Z{#$HG$o~6HY&#1b`eCm)
zqweqS*&`<WTmvE$8STOIH)pKhZ!B<HteuR@90%nfB6#2e5oS1oXF>(!e>3Jy!}Gt|
zl8EDJ;nL-(!0yC?gyf=Mqr!Da_j8RO?Q#<L#?kD44_$x}o8!;jl+Ec&#`;ccPS<6K
zBjFW@Q_3QVY!|HnM5giyH7CDs%#~(Hzb8oa91#}$(B3sii%nb*k6Yc`>3Fgw_KY=u
z#kKv4|JR}*Pvu4Fw#$>&%>M22wf(QQBxHyACtDJ-+cM@0&}}V9NG`Yr#e#&?#bMiZ
zE8+tC++Ml3bw;^k)uvaO#MH{awBT+W0w`|f8OJOcA5$PiQ>{~Obyobr9_-j9c!Y}Q
z;Jj;G>{OlJgZKTL@g8c67g1J|cn`wc!cwK<3)GEBWr?Lq?@lUJI%52-nuB_|Akou7
zE&M5tH;z`FGuU@{vFOdvxDf{}8jsA|n!T83tcc|CLsNf;!jUZPlO%p=^zeKm%WC1}
z$|SEgY0Q}h(S$7t*^?h<lovX<Tz=S{Mp|b&idpz6WAjF1_S0gs0!3#0{o&ZMGHC(m
zd1DSV3|?<bA~goqi{~ir6G{lqAC84z`eTOWgT@l2g@tPW1V=#xuf$m^lZunxXUvL*
z&yNZcJ>A%X$61H{+N4>!H@7xZrNM(QOSs!PQlPD8eqQ#ZchN&3H{V1bnUY64%|Z}l
zU_WE5&9uNC4yvF?>MW$rhSxJ>bH31GwpJ#gMW~G&Ok{p8b1<-kB!Bs7W9~Iu_>?UP
z*;jwnmV}JI*di}0{+h>YTYHSP=WvcS0wbiCy=E)sFX9i`sIIzhVWtWC0J|=Ootof=
z7VQK#iZ4${`F_I|8F^3l*9D2i^A%ZIsc~_-zY}<Br2RQ5$5+2G8po5+FnpK6y18#)
zWyP8fWXT3Do=$G0nZJKlV{a&vI{Qt=jB1_z23r!cvtMgVLUwjGssE6j{dzgAah=1(
zMqX^?J2vY%k07Fr4no()+hYG-*phAQVPj>b9bHh}>`%Z@WjhH*v1iYl^-%N!HWjCk
zP;+t4m?_PU&Il4cox;M;gYJ6}ryHQy@gk0@FN$KBJ!&~EI+8Q~R2y9_pY<9I9NTgP
zvs34I;&JwuNsE!4F;-$)Wbexq16i|EEUQ|J3sJL8RaC4KHe2D&un6`^UZiErxQ6~y
zwj`t%sj>kr^b~72!AzoMIbvmJH1CSO8|}}gFS8-M!&n(<4PiLJ5TbQDB2nw+UEvC%
z$R}j_-)79IhWz)~lIT0~dw?nSf5JZmeh~-%`Y`W7DA6qDoUrOHio4mXjQS^y6_FNo
z9B$!DLh}WYV#87L3^C#e$dR;0yK*eCH00yPY-*5y%$7tPa*Od^h73I5Sdfrh%rif*
z-Ap)k0@tM#=^TInuDjXXn!K<)-txRULUmMjHgjk;5)4Y%u^hhYKa1Msf>LI){aa%&
z)0*vA24~0!JxH_lH#Lfzk(<66Fu@gS2nQ@|b`Ib4pQ)ZKq4ePY8VjKr^<UbO=({=X
z4sGIam|#~A-|SU{Xz(7J!q)5iHAmf@U<&GKnMpN?E5{O66gL^Or~$p+mc;)Ppktwm
zk9*WBBmv#yJqG$-V-=+B^GQI*0u&#2wMQr1uXUF(iyF|k+mh%z&^umvLUiT3k#gpx
zGp&n95l-_0L+cs~ofg_1nM>1RJVRDmFfE;QFwCi#4(K=9KWWUHhUsIrB>Il&I_ZXS
zVr?_F`A&PvXuZK$oU~{S;`Zxl<OAv0h}dz6E5}yq*nF!oCmJ^2Y)c}JjYUi+!$Mw<
zwICt6xX(64N?c&s{#A>+Y=4NSGR3@y3%LA+lCzCsHCL=7prqVstxCaYc<IT|*(0O7
zBMzqYBjG~)&l{tdY?7pe#D(||CN0GOobk76=Jzv##3Cl34n9c5viWu{SgJZw3$Jyi
z?9-82qv4f#J8p2AeQd43js*LTKQvZZN}xK2GDRtwSdYqulKF*Tp=4puuB=h6N=h<)
z(U@Hg;m_NWkX`$8Y$OYvWZIx&jT5LE#n>*Aqy`M*(i;{g=+^pMXCu}cBu*9PD$E0~
zN?Pc@(wG?yq<`-_NY=}CThG^K+(up;spr54QYEi`9$(K^jMr{s6{YQV+cGWYPjtC@
z4o8&u{3)O%9EZ8nm?I6ej3BXyzVo{C*a#6O-*8khEnX($F?Ul2Wz1LwX+gPOY@?Gq
zBbn1gWRkqz1ICPL*c`JZA-&#VhIgU8-nK`vVVMTMpoW9`6m3z8#1@_2%^1A8v4Cm8
z+n$+W4zoeM1@$Vyq1vUAJYmI{DGj)iEs4GZH+Hn>PS<d$T=8kq=^Sxss|W1D0EUMP
zD)y{RwrE=nd=YBXgQ${rCW@#s7SJpxWj2qu8mlp_d7MOnb*Km7bXgCukU$Sv)&uRi
zV?D5M4)lPQp&lX@mEfk|Y%GUnQE##(5w|Fd>5-ga@nB&=LUPfkFl;kty>giLkSoF%
zqYv*r+OVztvay2G+S-;3r@_Emw#U$(P^|Nd#{6hD^YgYO`i|C-6J8B9zzr<JVJ3w;
zKN?<(XG=7D!m-yE{N)l1G3~P3a2|2XZI6z_pibAs&{L~MN16qp$d>R`W3{EVgrk}1
zKyGZ|Rpl0QE0UXQfpGVckUM*k<fbw}MQ+90B-C^KnKAd8-Ta9yiN4#-ChFT{BF$X!
zLa~)C<9PI#AtX0pWCw5T*Mw_xhI+QB#!xv^ph{S}T4T(P2GvSi5~-oGpK={`akB)X
z2nrvRy|&SQyR}y<M(mKWd};gQHdI>YF3{)>{*o{AT9vd6wcnT{4YUzKViBiY>mID;
zXqygpwu#{}3W4^NRsX25plMY<h$K_U4-JWO@c=4MnoFHG=0#KbD{M)~o^qOTTj;rz
zUp0(?JWsJp6rA`&#^$xg!lcC}C@CgxbZ8Rlh-Qsh(V)3#OQP@4tmGRxbXe2}49R<p
zg-8p@D*w(69gc*l#ygGK(BSw!TM}_NEanMvs=-duf`sIvPg9M-GZ--%PRq*`s>P?;
z@vXx8V>XcAGFEF^16iNpLmL#1l)xilh5a{;S<#H-*KJ9};juti8VN7_Sdfrhe8e_s
z?XUQ~dU03$)}8j+J+IC!eI#7VTkR}Ns7vA{uH}6^X)SNS_**r@S|&&=VsLcbaSuZs
zlF+n3fGdf6tG6?EQ|{)s8LJ?<n`?^iT%VazIK@@L#A(8f!E2Hts3BvvG-PhEB_TW8
z4hDCjBdF_+;BuE*87I0?X4CPz$Z5V^IvP*S-IO6bYb<Db2<hsUkkc53G7Mgml$Snb
z%$5e>L$)OP4&nB5UapNLOB5^2*#+ll$KbV%MNA9cHK?ocd5i(4EP$^`+N9es=1D`Y
zW=o>)$Zhq*A>52BYEY6db?@1VQG2_wcxh4F76eDcAML9R`3qyDHAz>jf3Gn|8fINv
z5~(q>PR?u*S9_M-Sp>2;KrrXkQO{D1t?2Y>#E^Z$Sh}>3ZOv5Z;!(j}R^?7XEFtsx
zv&IZ*i2bx6v53LPPOccqUgYb=ae5c0!9|kO&9#ab#oy5}EbU8-)0d6ak`||(*waq+
zE<X1-R*t#njYyJt6}F_<<V(gJYQX)zEeYAxeov5C@Y4=9jfRTob@^O!6Gm^@O?`^v
zJDW4ozLHA0)?gD+LcV8-F+&<ySJ;w>gJm)ClXFMz>lP#=7xRR(*4~+xFCFOv9mPdE
zL#IxjjaG1W=5NlnbceCt(|Up%_$JCEubr)w>uO+|unuz2m@mzw_6ZU_9ruFw>U+iM
zSSW*_1&#`xi6urW9O6f-9qlT%9yXeoiiq3JFBRb@#T|Z0tJ0T8ijjSlu?Ev3dv9h6
z=YVA)DJ_+dq*<0!ESrRa^0n@Pp!7^Ezbl?U@%Ye3jQQ4(AGakTyYX3D64IHl{r!h@
zCP$BYjpD_iP?;9J=%C3u&I1)TNhML@!U9+II5O(Oows?f91TDgh9sNr8;zBp)^v|$
z^1kH4R>g9kBDp9A6W1NMd_{ab-psP(iVY|1N_*azea(VjFG%$CT?>DF!VSVA0RKhI
z*l0DKd=?HNLaA4yU5=IT&DWSwe4nuz(xP}HqsZKf$P(Prkx4iMU2v=7lN4S4s4;6A
zLhrRDA$`mbGRO;^`8sqM^=RV2tYQm8(X<EUH1;@C#Rm2mYkl3rNfCPKj3o^Q{xim^
zNeld;%%m?{7L^`>Y&Zl1qG9Vh*6!8L9Euh(hlKd`)5Z*I=I|+75`8y^8;=($0U8A!
zRl1>eBG<yr+nOQ%M`Ll*LhNLwh~F5RxGFN_H-cuua_8R}^QNKsH?}1Dj^=LVCMa^z
zhvQ>+SeqUlo(<k(gs<JvuSw>unWnr8iX6Arait6qtP?6Cw;6M(VZGUwL>z02NKXcf
zJm6c9kX-Q0(}INLf*0B>NJuW0*k)ab3+$GBY;o64N6+9)m`d?!F$VHXjn3ep<y4$8
z*P;!SG?P2!<_bm3>`qc~A;rzYL|b%0Tcn?remPuib&c&R*+r5<5?5P)BWbnuN#k$T
zI>cjw#3II?gW(A%!SNoEAxVGC9_pKoMNO$-+_oM+j6`ND5^K?(HD*QA`qQ=~WY?Qv
z;CjWA)~TmKT1xdU^-5Lhj~SmIHkK$oKD1=&T}nz(E>B8O{(vzn8b0r0<-P`9YWH*9
zXS+kCZ=hoqc9gC_H+-VI;Mt^2<EVyr_`WBaoZ}q!N^C7Axd|)(SBxc2tNhKGCOFFm
zH&F$gg(EcpNSbo}k}*RX1fR4eA-l-OZAr*6k`3Xq#>ja(s_j8j+s9j&xl_eT;J%zh
zhdM_)HlBYr)>2yIxdxt<dCUep2IoDgE;b>m{|959H2e8mTN2WK`h^@kY^jaxo96^a
zT{(Onek8i+Z13HUk-I*#Fj2b}babx4_Hmy_dD4r|qtf>iV)Sc_dD4*EYD=Q;$PJzI
zaEr3s_j(dhBK9UJr@xHX%9Qwz0-63b#`UnV0@C)#-Kby*BoO;zvEv95s0WHReF+qp
z6PDWVH)c_T`CeNReTVtBhiUtj?3}(|^*bq+c*>YW@Je?KX`?q8>}g{)qy_u7Oq=A8
z9oUy6xO^k4ilM-rut1kLW>SOOwIz`nZtIZurtww{`D7ffnbXc-bcV)H9E~lvrfARD
zc8q0Ci>-s9HQ-qXxH+9y2iSyFix-VK(*S*gAklN?xZpAJjb|G*jF;l%eZ)M0^kFxR
z`rLV2GeRFQ)<b%P5XTe0VueZa8=@6KC@F~kF=O5|jDEzHgzR)bY)e80(QGOG45F>O
z{`ymx-c(!d3R31xY`;2jG-^f(&)IPP&{!>L4QE59;`2o3`=T;E+L;q}b9~X57tK&U
zFG%$4%nQE+x#HZZaj^^?)wleZ)qLg6{R(jhGL1@8tRSf!aYa%`{P)Y(@7HWe$d35W
z3{kIykL^5i?dZ|bQ%;Q&qh2ElNBkj!va@fXtfSMeQEU=&ql6_Zl5#y6V^%cNcWg<-
zp|Nmq(noR!v>+k5=+ks{?b!y^3DUtDtztbE@Wvmqfjnxg(zMQT9d0D`xycG{6az^(
zb@aS3Bbs@<!j?qef!RLVZq=x;Ea&8lQ&S%8ohT+O@6XYW;d-sHh-u-vCgTP?X5&06
z$R(UfI%~|6hTKJ45~-214m-B;43hw)6cC?va^~cC)cf{q#jw4{SiZEdZS!Z21Rn9n
zXOh&)$~KqfW2^LoWZ!Abkp|lL2@;DKh#bJt2<3L3Cj4_v1I1-i0bWs<tSB&qzi6zm
zv=APoBNkXLBwvHcQaJet^016-fU_D2B_8DY^TzyY0RNmV3E3-uhHYe_BcZLMsDwLN
zn=Xn(fK2K|(eBxb5&bh`@zNr?4VS6rvt^$@C1n>yEurG+PmFoeQ2S$B5^>ZlA{-ec
zv2(B>A-Uin$%2IBf|txKNJuVTX}dT<T%c~^@ugQ>(bGrv%)D3+S2rIix3Pjl)p(*@
z^x<l=jRIk0k}+itsw5)ar1U#sX~gg?3p0fynG;JRzL-=RahvhCYBoP4NSLeHb|$vz
z2nwya9(S~k<PoY+;zvs)H(}3n%2?uL&!g!X8-=Jwyve(|Bxhj&w>oLk{E#s_8UQD3
zNyv_N)Ru(o?ra8^)!lEo94^q^pIF@Pek;tcTtwGC>b6{YbUcP1tu!jy>A!L{+|mI3
z^SrUd$;L>M$2z@lCeOhW@^`)7_`5Vqdrpw(+0_?5{k1wcKVGlKjy_@$-)k&lN_BSD
zgy+2rPBi8Ipz$|r%6+#j3E7RmpYd7fh;Mx`cHtD`7EV}<yW524jK{ATi<B0R4H<Ey
zA+6hmJZYf(it*QLp!||8iN1q!-Ps0G8Beuoq1_)2sb+Y(QEON85r5N}yD3BU*T#aT
zh3b0bq7+WW@ThRAq4rf{PBhg1%$7uI)U0Dfu?IDWv$mU_gSAwrHB)oc;@Vi^aQ@~D
z--ewF6Fq7JN#`c9WtBP3g5xYW&iWi@sT{c3)gWAF%#8-&YC&QVhng%3#ad!cOWRSg
z+hVp^<S5fmvx}4=)jsDj+($c@;kyjk0b?bl?Qz3YP!`C7{>HM1j;G2(hkOWGH2n4&
zv!>yfwIw0D;H_)|3q6_*26gMf2ol9of6Q<_YAjG%xYpx%2vJGT$Mj-M(|go;<8Rjx
zd4(;BI3gA?zMLj-zqTMDxtJ%)v3AXy&(Uy-%dnmEbYx+Ta!Bz|r$-w$k>`yyoYq9P
zpdgKT%ZkGb1KzZr`+8#*G&^}tkXS^|yZ-EKt5B;t#VXuZxvbR%COl_-|A?`$Y4yD!
zQx`np^zo1<P1iqc{Pmiyf54W6><#Z?xE9(=Vo}ALYU31S#-?1HY`6`rJM?VD0R6hL
zcxeHmr5EBa>*FN((_s3a#!P51{jx2II7}9PPr6R-m=+`?7ryH(E<?ItIhH$cNu}3)
zOE+=z#>%U&xPrEHtj165<ygx<2KmPZ{;^5?=qy7Vh*j0D$iQ@#(_S$$l_$DGr#mZ}
z$j{|zyZjrv_^ahnz7D0o^w&TO_hjN`%nS6V*ICVLG2T?M%x{;L?n3rr8U3>Y|9G7h
z757peXK)p|4}Amwb(ZJo`f}WH&>0+^#6*yH=!@vO+gaIgXM*RYwRpd?bh=!d?7rAp
zRw(B4UbXu|XN~`r{s%91R#e-SNlaJr-7#cTu$c|4)T-s#?h9-cQf-}OO?*+#gB(k#
zKDc|Djo#PsKfz}Dsn?ZZjK{iM1|F~hj}-aVUwhjX;=crq-b~8eIgc_AQvtU(L&5HN
z5J~zMbD#6X1u&H27Nev%*jHq`PjZ#_XnER$uZXFCkFoks{ppe<{g*bA09)|S>C#s5
zuSldVlG0b8{<V$_|2{Tm|F8X#zYawHIw=0y=(kkE??|&l_$#Rc+)vVu(mzM&pJVjT
z3Hs+j{4-a7xK$jyBUk6>{GIW3y<Wrq%}4kUH9rn+#-KU_%4R*~KQvUgNA!G04?Rzk
zPV@8VG>dyxkZwV!avpvY1@6bHO|ODYXwE5|CsD(#d6fGV*A`O{A9X;b|6FRlQ>b+%
z*3SJEuO5nhmK6I<^C))P(IU!ANwc_bt2K+ABDgY?{d&ZSf7^d99o{8$I1u=hz0p6}
z$AnMW$%KEBmq~IM|AqAZcKYWX^v^r#pLgS*xqQXuqn=j}7bpW`j@j>fL8Sj~!6Ld*
z0;F3g31&e;21u_HB-~Q^;KvUZi3==2JvrAB)WWZi44e-GD0O>y>2DF2aWwL)fUP}g
zB2aP&PJ+$1!t_6kze|g>f5Dc7gzyuBM9<ZI+hykih;J?+Ut4o<W3hM9#R1Hm6ouF4
zxbtB?&5j%x0;e<0axI!!vZBB!{)MqxQli*7m_gil0a@J|N@Z9s9QZ})X)I*pI3XNG
z;vZfIxhP{;Be{Is<l^F=8Z)Rt{}o#jap)}^fZuy3=IXsYEy_CKwCTJ*84_{B2~-eL
z*bwQ+SHP{X32YsTcI2z@1k<vm{9_saSiwJ5iXX`)Kr$wF<jz3D6V<Vp((qSqGX6>i
zwX>eCVMG1#1m=Y<DkJL!pW=E!aS?M%TOLFLr-5Li)<Av<mlyB}lMy9%dNpD#+-a<Z
zl#1_c#iDY+9WqUlJHK!2!Z$Myhm3jfNeqW1_FIzZ43x1dGJ!?LiVxz5pwzQF*gDvq
zqBqdQa<qx17i@`dHJ#%~2s#L|J&H1&{d@QCk3yIBC064T#yUs~<*tlKY=HZ$8*-nW
z0{7W~`>fzzgZ9<NJZaEAYD?k~yvUmA6Q;|PoLVRRl)w8qWA@XcvLZue+2m9;wZG2z
zOEtAWV@o2gb~#|p@6?%#&Z@~aGUcra>YEYYYEG*p*;#8V(GQCkORrhn@$;T&%4v`D
z0OsEaXK>s0lr!3BxU)PXuh7Nmej@w0=eF97*s@M(69!B+#*f8Ru!Tp|ZT#aJ{&5}u
zxIz3#HbzoKY?v)MXkWSs|M(zk1MWwSzuc$PbHM$u4-CFEDH*x&Gm>aomKw`+qK{bJ
zUo@6DrMlx4TSECrNUI0&d<tLAY<%9B4c{C5R!HJ=mL&A?^|uA31s`9t58_JT1`Vqy
zPo(PzMAYeDs89%c4#Ktz?sTkwbPV5RrCzl=8nG@|SLzIBih(S_?c?A-xLzTU1=T6O
zEDHEx-M<9?8<xnGB=}#qCGj=9$l96*b?ZsS=EH6iTi<-h6Ww2@M;m5rSKnaVf~#f0
zTL9ry;F=Y<!b6WFTsIr@qxIFB1c{#0kOl90d??wJTdB31j6^Qp!;+gY0uLHXoYqPD
z=aw;Nj016|0w9alB?QNf8PXs)VoO5yn3r3UND7|VB4qG1zpwHolI+aWfo-QngQwe-
z`mFHPt_W$O6CnLD+sre@%1LW8>oZs)t<Ul{2j)nzm+-7BX)TwGS<$SeZA+r>@a*7Q
zy_6BvUtK$coj;;vFjmo-<^_iE-Nr(vg)rma^@U^zT}z~loi)j|0%~}&l?3(sjhWLx
zeY-7*)Sy}qnww6zZA|0cs&kA^f2s-~qa81~2_yGEj3rBp+~&-*&so+}I8(3+kEN1W
z{em$&8djeWBo=SP+<o$42dk|}Bi863xmtCPDD5cXgj1@V#{3S~a0u&-qGIqKL-nV|
zx=9Pw?o7Fj{lkh3BAf1uK&f=xr@<nq@Fk76UomD<1MthXBxGm%k}ZiUUSw^v87;st
zYe7PC!INSO5>gjGVH@xh7g#5B7dKO~-p`cCdXPxBDcwq$2~kLsy)`;%+k+=qW*Ful
zd-=zH{&7(JNbV0LU_AeEJKC4-z&~uA`hvr)#$WDJ>Y4x8B`EgvTmR4VAE%8aPN{A!
z|3S*9{KpdMVD7`0GwNf;Z1@i5Xh`A#OA>ni<Cvh-a}s7fsBeF?fR$Qo>IgOf1l!u~
zk93C49PL<JEn`vBg0?*~10J)*CPs$XmVlB3Fw9{{0G~2uNdvITYJUyvWN}i3yGEVk
zRMt-C@!%F7aRnTtrX_aTmVzk{Z(NC@llm>j>U)e8lNPI?jGLX3{PWFj;GdS#iT`lp
zO;?33srS9bm`4q~@3JLP!HcY^?+_$<c01eo>*k=>xz2R0mgj5IVm+=$8|Ld{#-gP~
zaC6w(+$`}HTx6#7G<les>}wx2W=MnU!?q;iaLFKM{(+jgNXmY2*EgqClI$$J(;g}<
zK^0TymT}~IQDTX89Xdq1;Z0-pMigO+Yv&wsc&`)n5h>qc`}!-k`+uc?Wf_*^YgjIf
zl*D&dH*>`cxLZqX80!X?8wl>AkD|*R(YHi&^-q?#hm48J=gtEo*qza6j#LUIludfo
zkznb3#2?y%k!>Vj%Z;2wxi6NyPS>zccqAw`94^*ptCL+(L;X-={gu8G@RdQb6?UlN
z;8F)60XHx)m9)Kaf5yK)fss~D?%{r!<zt$oT9sWj%oNLx`WKekF3G6oPuVzn4T=ly
zEjONNOuN<M(_#^`-ALGLn%IJ|x#hM##qOO=nOew~I1pXz8&$@&#HezIF+ZB4ywR3~
z^i0>;lK3NF&x&O$Ffn>x5Xr^D`$u+<H)%7wJ#<DdR(w#+M`!lN(vK<Hv&@eet1WFu
zzlll?W5?<9LQu%EVp(BAS@!F3W7aggIx9#l%Dm7Y$CjSQ&2-UWia%m?Ut)&DD6|x5
zp+am7TJ|{R15fUcjy}PMNsjtkES1#ZZx;hiX*r1S5b@?$ZuL8Z&#4&x2EzM~qLL|}
ztKymTptRPsk<TY66tnxotB=%(dudX{-L_PYGBPx5Nl2ei6C@UV3^{bX*lN)LLg{P&
zCevUm7hhp~l%fXbbUQ~84J}BbxGoByB@G7qCyiB;7VO(`5vGt0ldJIZOG>5EW;jnA
zA&-51O)pzz*(kwEvZ?+gDWw09F~^!mdA}`*xILJ35}yk>Qn6pNAR)P!XQZ;72ksfi
zVOeaKC@<$Tvv85}`35d1W@Dv&_s9c?&_eZS9kW;22ESqqM_L=qW}1N{VJL*^-7M}$
z6q1PLG=$nMK3qdFy>J;#4rO09W>Yh~FWHjlJLEUgkaxI-b5*NO0i_QFv&igtmt30D
zMP1Iktr^&XJ^c!D9Wjzj3VyRbznsYl_*JkBm)Rti%ZxeGuv}tGqVHI4c|ZY>7vt!(
z!zhHk8ZmOi#=@oTl3OwnxM<UgU{1j+yxc|Nb(=9i8eT)TB>IlmJz@_f^*Ct5;Z5xn
z1mr1yg7d=gE0UL9zE;`e)Uc@PHYndb?a;pOD5#f3iIE>SR#RH!@4>++*eeRo#o{W<
zDGoMbIq6ql&a5J4R*0#XM|fR`G>@~!Ol#(G%9cdm&EqZ_Y6I&y<+;_SGmcOZ%5;u3
z8rUp@qsgY)Rj~<|Y~=`6;>+G8>;a1!$7RrA)3{`;qO_)QH@0L58HHt3NXBfFWz2dc
zBX(+pGX}B~TB-MCR4gOBE<svG+n8s~G8(odQd@@gGTFefOI`!}+~XCB%I6H-_Z!QQ
z7P^%gFXTaiBE0`VLh*KE9yBPvSCCkgQ~;-J_67>R+fgpAzlh^?s65o)r{GmD7Lkfh
z$l#d2&TxIgSXF7^+8e@!%}zW$YuB^BK*0$;5rI@tlji3?Ys{+#>rdN~kUi|bvt=yw
z(Moqo{44GSa>BDTBdX0*gLTim<6WwkYvLgBNWMEVExHUkY!P2ER#94uxJz0Di<l)5
zvk);@8qH@z5wj5yBc_qC(*0#)rZv;}k}Zk8o5l^&G)~iA)$&{`0rNFxkO#8;nlIfT
zL9R+>!#S~<bXhr;uspNOm@^H@CAK8uNLpk}WYUF45DOBL3r@ONkdRz(sgnf>$;HjK
z3ueRxs@f(Ox2o;lWBw8cMX0#;a?+hFit}TTcc?YEW<ET>3a7drRnKXU#IdE9Q)!x5
z;5=h&Qprw{bdgv9cOoo+E6F(Kr17_Eec?etqGue_GtF!rC~iNBYqhY0UF=Q3MGU_B
zwL=2uXvZqA8VjFN<+z|56I|j^B;}$!h6Bx&NfF2eW1ciX3UdRp@U@5cDK|4w^yT7c
zDn^HL0Hg;TSyIj6{5+gpMIYsh&0NF71?xH7pc&~9oy(-fz<!sp64L^EpL(%xC@ZI4
zk^LcAF`r{u#r!W2H7i6_u}?z(JB%6E(0`*X3F(fXX9HU3_;UkXCAN9A&{2isJbFB~
zMx4}u0sXMCP-y|(h*RPNu4pn|&re|=T2S7BE}FD#^ApDGXfS=ymPG$yI(Dg!${jv-
z{GwZK$0y_)5*jc}UoaLbJxmDO1FkT|6I?}LN(l6R*O(m*rq9}v=sQe1(3`@*<0uMQ
z8^y^s?O1V+`}wn2?@IFm!}twjq0_>cq23m7jVmW5*I`C1*8395Furcgo(Ai`*plcw
ztea1H9v|z4F;fgM7))-8j*&guFnBi)FU-74iz88tRRM2eRFkYlePzN*<4wl=XlUJF
zOQP>+-H%POxOa^%-zERlpyMQQdJr}?`X>Sh2<{0l+o0WZ0$>~tQi<Yv+0@tq9yC^0
z+U|QlZNX)EDUA}y8-_p9EW(>Qgt%lgLfnYQPFTu3Zp^x7EJtif^xat2o`YS~DyV;<
zlb&PVIsTBLtQrfG7Rq&*hTsMjE6hy<nuJx$3&yNy&=hP*^c|XQbaia5*1%DO9QOok
zP|*(ByBWjv7GnX^!nIx8)U0qAj+apfoUoqwUB*mlz<q};i8yc;St6Ne;(4tF3CV?L
zJMK#^INfnsTpYg~E|BiJZgJCHLqV0l6CA0}7h<EDmJcRHu^Wa0w;bs#r88k_>+>=5
zc?F(eZ|6Awc$j}Y%0FHuek6Abk~@~#@=djdx3~*_&iK205~YyDZwnHOn9y4HsOMcM
z&pPA5jRleA7VRku`p?FKrc`QYFf$|g!TN}QgMy~ze=z=XP0N2fx0V+^OSOr#>>N1g
zjJBt7c(}8Fe|G=v5tHpCH(`BmABo1*3ueT)zm@czJ#aAV3eK|3nWpqD#vEu$-)KuB
zANGzxL850?*S1XadPe6^h|XQvaU=|J%srjI8HH({wv5+(#-gU}A2;}T9SZTf3+vB5
zui5><nUB&Z-EGW@2HYLCB>E2AW(Mwd)D#489m?Jn1*}IK2Ff)SEiF)6e4uVu;33|$
zp7o?L3mQO=*^=lxKwBB0I~0KS?a%IuxxSvQ7^H7A7B4MG+kBAjP(Wh-G?>1{m<bK0
zZ?Yw^STNnG!gN=5|AA;v@?p|jF-$*dES@P$cd9T6{xq20Ys`cO(+}E`SS*<CQefJD
zd-lNIM40qe4AXBIi)RW`WJD7DX)yhoF%ue0zhX<G?=Wo?PVeAe=M0Wurx4;m_Kqk-
zi7gnO|Ib*gwD4?VXLoRK7I&Z#Us>WygXXV|8PK5lsx67WL&L$q!F?e#`}St{9gKq0
zqYcBed2hcWaX(-<xG#i<dD8&eV9bIB&^lWZiv`er3D6zc{reIDinn2a?lKlFZT|@X
z+AjeTylDX4Zp?xP&;eT#iv`eu5TN}Bv-|H%1SsBy0eZq%G*f^Mga9#b8bGf$W<dk!
zQCkvy2Z$%A2M>k-9oUyWjcp)eG8=Ps^R#8ao--CTEnxm+_Ta$~Fz{OSdCeZw$EMdA
zv!Vg_j4g@21Lu!Bhnx_tJF@$dU0=Kn1NFnkqNN2YaD9g)I)XQ?XZ?UN3mQQ0vL(@X
zfVOa4ce?~=-@)v@nD6`ld%F@Ksj4dq%)YOHdw>vwf-@`vYH8t!f;f>yghT{?dYkV4
z^ky$GgN107LQnjPOIL}wAf=^c8H~Xs6_dC{F<5GXO95(P5>vz^77=4iG-4&^-249g
z?m?4)#Zbk2=e~aL{&VkJ`%VA%z9^Gc(F=!d-2Yoeqs{7WhF3+(?vOfq{;)Y5Qb*4o
zsfwYhqnTbEO`USu)aj-3lc}RaGHD&{KWyU;w~l6db)@VLsiVIfHitv%=-ne#F}RMr
zKR5mFZD+&68rZSbo%SP8;jwuCNca0a7W!9}FU4Zlmtm3f=bR?|k=Mc>ye;>pXx=lu
z0Do2LKFMClsKs8ysL{TJQH{NrQImZsqh@;vqe^=zqgwl$jN0sP>1UTQYO<GUbUC9|
zdpV;KayvB&@G3({ya=tkT<)yF6L?-JwD8w9$ilyHbvNgnMiR}00`4E@!P6<eco}kJ
zT=($n3-&tvEfGIb9KOC_Z#A%c_=bZ0yeo`=@SZD_L1=~-S)#if!gN=tfUwLJDj}?M
zg(?X5yF&GbLe6ood{!1Cz+yj+rR5DLV9|zyW&uY4o}sV|aG8O^%~7Eo>EBma0k~UX
zCE&*fGI!4LE<e@>mf?XGe)!KLMUnY>g?!+H2Fg=jas_&z9fuVlKdH<xP%?#8AeAi!
zO8RrIz``G?K$6Gd6&~mz$yo}k0D}sv0s9r!06wCy7I2q=qWdFPpzh;XHD%p+QKi__
zsRcE)9Hw5@f*d$a25PQWNK@}qNP~7Nqz(HOl4)zJ6eH7_3dwZ2LNdKpA(=j)kW61x
zSO<7OVLjl;Hc{39c$UJ(^+h!^&Csm?-P`zih0y)6Lel+>Lel-Wf#QqF?UI|^mnbCn
zH44dni$ZdLP9eGf(Lm8rH&S#|chjGfU4cz~i7T+FuW<!7^)0S|rtY<{(~TXKu%mnw
zv{s58rzoTy^A*yLj6zc1q!83|&gWjat+iPNwYQ9e+A2}|BZXAE*FdqS=Btv0&*)Id
zXDn7o_w*{Hd+t_9_dI2w=s2JPebqW%)KyFROjn?<A}X*2+^CQ(V7o%LfY%L_qMx_|
zih3@aZd_Ik+diNh@EnwE8?8`Ep5Y37XjBDSeUm~O`j|o*x>q3${Y)VZ9e0c<grVNE
zXP9T#;MrI4^BQ^fy$bn+9SZq`cMTLq^~Xx?GHEjBsK94i21=&uT!FNIr~+B-F;Frc
zbOmM_dz`4PU}v`HxB?sBY87b79R^DACtZQX-&cW^*>b#SLPIHb_Do}NEeu}89JONb
zoeF92(+X+uI|j;g+D?$%v~jwDlHQ{N&B_}n>Dyd^yk2z$npJk9Xrd((6q4`73fbP$
z3fZb|SBO^SRoWTGhC0}AfS=ci4I@tyNE^;lNE?<LD4ec!1#)`87079i3bgK^LK-yo
zE25H2=O`r8h=HQ=MpvN9?XEzTe^3Di<(%T%#<eHcgZvd=gZg@s_l!H!7}tQ$&mM!%
z8^k!DLK?STA&q-jAzANINY;NdQ231=D>^EqonEK{?XeA%On0h4<DM{3(%*3f8dt>;
zrU4@ljho~OXqq`^;BN29hJ7R0--qR6ccZZ1VW22|&lRZj#45=|yJi_EnS!psO#QCF
zCqLo}>=duL0!QSJU4e^_6RTBmHy0o0xdIj+cGwkaAZ${B&U`{4o%xPJI<u-q^w4FK
z6w*tJ6tc0dQpm=7r$P>aPb=gQxKAM`!P;6;#!0YKAtpilQiV-`eF~ca?@`zS_^iTK
zwvz!@Pcg1;f~!xcgWe`_b(caqv_~OryFnpsdsHE9d(}Ykcv-#ZkY1^fhF)Z#WVuQO
zT79>HlKyj7U}b%%0{t<vK~!Q)Mfw~S=$w@b;hZ9S+yzTB?0JqEn#G<!E2KS*jgm@x
zCL1W4m$(8Szg`7e`mjP;x=SIA`$!>;8{H%dWr(!Tb_HtjxdL0<ttv=I9&o?AU}*u}
z_n4tY=r%VCB;Dx>Nq3n-lDdJg6@|75kV3C2WTC$sNW*ew72gvLd#iY-84LR?&cCsu
zi9jqD#H%%H3-bM71-^-te;vPKt7K5kU_6798O&nP#b7pr3mD90Fpq(sL6E^(2J0AX
zVz8OPT@3DFa4&*T#%b<};`KZ$o3zp)e=ZsevMI{5HzHn9K5W>{Lc){C#*;|PlgQ7L
zNYs<a*po=zlW2n{(HKvnRh~riJc)LC5)JkwTJA}7fhW-yo<yg3620R|bkl&8a~i_2
zq#xdrYReQh7MvEX&rq;9)p*XS)fYd5c;%OVb%qBds!J!6Su4{Q_a|co`(!xTsa&km
zz?si{4R}5deUAF^%gS(<ADgfRPd+acORk1o5yeS^S%5m+@e;#5*L>#(Vik^DPWa*|
zA_r&AcbcLZoW)d>r{AfuaM)5Tm<}ZhD+*47<x3<Jv%Qp@->FLaZM<eB&2l_gb`R4|
zp1{?Np{$rvg`=D)La^N|)q$wKFvc7v6@#5gWLBramC)?)irrd_9%X@4N(%U=qIsTz
z$Jqg(Qis@KB+a)Y44oP=enEOc3Klow*I}s`etWtQUWjLqQtzg8rNfYITZ*ofa5SoQ
zz8wSZx8n%$1JtBM_(5$mJYQr@MKlo%^%k2_HF(1SrMY2%h#LloxM6^Z8wQBDVStDm
z28g&}fQTCgh`3>Zh#LloxM6^Z8wQBDVStF85D_;F5OKo*5pQ-y#0>*P+%Q1I4TBXN
zYev}g=>+>q*aUA^;gIqix;fGYDoGDmEy~mo@ntMrgdj~wi*qw1FQ(<ivb-3U7rXLe
zR$i>ii&1&8DK94F#iG0zloxyQVoqMH=@(=A#g=|CB`=ob#gM$%(VrWjIwI9qWQ~2l
z7&Y)|SsIrQdv<u~nXC+EP95Gd3nx?T8R7xsl**XTABu^gPIWkh;~!bg_T$hV8Rs-)
zQaFjMw<DIs>pUTJa|>rgCNgMXP=lB4di!uR?gaZM$n7*)G2fa#3r8<Xh7dl%QRz}1
zzK3cYY!)jx74zf?*}}C2`$?3upJMPdgJ%$ID6B8|Hx!&|RF-l9^FE$Bt`g2Z{~5AP
z7*y7&zgQ}F%ol5ap-6jW28XQ%A_e<bNc<v|b&Dm_n1#55weWd-cNJ+YAvu_`(`HT*
zDj~^rW-W&suL{KP#`%~#e}M<sJB1=7ay5h@?WVNHeCD<irvkq^N4MulQXa+&p==UP
zMF3Y}5IU6@5aT`_VA!lTYL`XCE{lj=77@EFB6e9s?6Qd1Wf8H<B4U?C#4d}7T^13$
zEFyMUMC_f2*gG*r6z|n-#NW_3PR^b6_#OPW8NIbK8kfs%ojTL#@a87kOY<B|%^bv+
zB%)WI9}Q&nFy@T~49vlp%-aR~Q~1GYI3H))#FD-sOJp>g(~6UO!niapW{G+$myXgA
zH5?T!Gb1^57=A6hqMi-)vJ*LV@xDx!30SCrfk-JIE;~zi@NM>j(|B<Z11#j(n#UcU
zoodV@!B85<+(z1+CY)jx$GK6y^hyi0+*jy#T3`xJ0<iEPoQ*4eF|xpE4aRZGw1pe$
zldI8-q(kPNmg3Y5F3=4X@}*gWFb(rk-f7dU!DNogqX{1Bji)u{QZO-OQM>KyMaCur
zEliUc9xG<|42SC)@!F~I%Je0&$Y?)XhSa)TI%e?)?{Q>|EOnY9A-TaFb7UZr#M@f)
zofbDK6U&7yz9EFfnn*Stv*IC4_x4Lv)YgNUK7l(AbaaqQ<Upn&lk?+LxU9UQ3o5TL
zZzB?|pj@bjwv5!*ncBQMnGR+wyk2MF3cy@8ROsG`sd-ew4_C^nF$fR}SSgq)E0SaI
zkvJ4<68R*P>*<O1!V)ZGBAX(cZ+6;{g|&_<%~-3W!Egx9L>-CJP(Wz8ZAACPr8mX1
zc%`v0w`=b+Cx5qPn(b7jbGS8!N#}Nr`}VHg+un96VSFSREI8#pyhMfOmdOPIIN>oA
zg#H>i@y?1R(Y7u>^4lXUS*Ui>*PZDQuaov}l$eYWJB&WY`IwW!sngKcNcg9J+I^-y
zk<z&fvd2$(qw__35+wUH1|10c?M{S8i`Nvxi=#~mubo?Odcp9TH#h5atgatYUQ-P(
z4u2)Qc3pq!bi-@b()NG-GQ4+4c}+9CI3AbqdS&bdmf<yQXY7NsL&H9w*RrG1zFc>4
zekh@}XZxpj8Cqxl;IW#nonwZQmg_CfS0%LGn3~#XXpK5&ZR(%5Uo(`nPB)dv`LmSP
z?{67tXqB&eE%UukhqMx>8(N&tOK83M;*O}HRl9%b1^ZG%N^6Fp#q~uAt+(3m>QY*>
z{(A3)v)8Q}Qd%<&Ev|P;Y27w&yP<W^4xjX4?clbR&&lejw9vuE38v6LUVd|GV)N$|
z;##Vt(62YX{Gh3qVb{I=jYTbk3z@MybX590zdFRDYh>kbe8FX23B$K1-SntoIC@Xj
zj_-W-yFvX7zTlz|g`5g+8olWBvfsW{G>y*s<eDi5&S}5mzfGgi#C7E$O<R|@-@l|t
z)4A1`_U+p;@uB~wsdPd<Is>LV@9`TdJ+=UIEa&5`%eTcQ+6yVo`t0h$_<gr-vKK+J
zFJZ74K^`mPV-H!7V9$d44O~sM;zM0@S}B}bn&z><6PyXS!7qk082CM{b5$bPVdqkP
zSWk86GDpr_o@i&VOX7?w&T9Ks`EU+ZG?kS#a&K1F7uczg)pKWNRd`}=9INsgl~cH%
z%c)gtr$P`KxQ6cJk~fIeI6f4aI}*LFfc*)qq49urd>i4y3rk;Fp=G%QmEEK$mf~ss
z8ek24c^O%p%By=5S)44#_4@I}pYXdv5NqI~&s$Tz1is{2feii+c<uy$GP%;iF%0n#
hJea|um;vkodY`v&k)GyP2V8s1MRckVc_`=a_z#j0HoO1;

literal 170462
zcmd?S37lL<buVn$vX;>{+81xvYFirF>KU!xjTvbq*&c0ZWE-&T;ZFCR>2CFOw|miO
zY#f#Zu%QVgG%th@2oGNJvOq}k0U?2qg}j%Ae1rr7Atbzzmjzz(zU0daf$;vP&b_zl
z-nu<=t28|?zu3Q@H8Xwdo<8UNPn|k->QvRMuYLQ~S1q}U{%db&)C$GQ<w37<saUU8
zroBqD)9ydgDwgxdl#kxl+4Gvtvz>wVHEyleIo<BdS98s}=jDs_PW$1ZM;czeF*IE$
zd6kA&89MK|IZ&M%IwgL3Cq1`bp}+Cg&`B@XY}LKf)uz`N8lRwV8XEM;H9GBOje1T{
zz1x~9h~%o3$>LO}y~Y<W_<Pw=eh|vDvU<s@*Nb_tA;eozb}LgYcgll|YiqMpq>sjU
zqd8mlh&7?BPP?zroGy<y3*Iz7UC*D6PgJWHgU>d|2IJLQvskS#`E?CwB3BrnaO>ku
z?=n=qaZG&TGz-OsLnH<V2RrSxWw%wKcCBVJcy~hu8WUeqtTes)q?^N+FVB_TMx&U6
zq!YzT-m4c{6XUIVxzpZMXf|t&dxnOlXZguswGM@>EwyU1cs*=)Ri}L|B$@C!&$n0R
zs%4koFYIi!SJ&KTVZ2!#2Re;TVP|`7-K$j_#b&iW%U|+H@FltGbgfpH1t*QcsbUk<
z2aDCz@RPQNi2cih^ec3#e9=VRg({%^X}1B!p|YuJb*k)*yA`)Q+r&4H7xTp3YACes
zj#u1EP;aMw|Jmw{SNHPH#H@3l>lEtV<o!DXetj(ng+nhpU-U4m#W?~9?i+Gn)CtH<
zR-yJf(eE!*r@iqSjAOi5fm(8%=h~ZF4R2g!RsaT7ZlVY19!-9MfyC$Q>GNWv(efHT
zx}E1+?JdGy8r4=k=aq|<i{p*f<Ye(Oee1GHYnn{?x%Rf2Tk*<`aW|hQV{0}bZEZZ)
zXb{;(t5&OfjmG#4ceO_635YN~+i0S1O}j-Hpc~k2u}Ri^+i<N`E<!&eZqs#!Ntq4U
zpj*j1ez$dkehcNht!AMLt!_G1tv9`j^I!=-3)zQ7hwqT()Mo3&sRB%T>4AL*_TwWV
zQ@J`-?X*|WkHK062H5Xi&XrqvQe+c~1z%6Vt!%pOw!va;wlXoED|oq!wJK~J&S2%>
z$k_N8><#>`JYQ%{Pc#Pe6JUq^M#pPk570ryMR%;`#sk{Z&@lWB6th}zG3j0^!UYH`
z5g$yr4Uf7B6ub_$%iY{}66EIk3bVB;x&<@>B7H#BBQu1TAhZa>=~lUko{$>{J`-M@
zzU{g87E<`QS8*p`pTt?EA^JXvxC))CS{a}8Kn5-(UoDs2`ZRhNdNE;MoSX$>jn+g1
zbX(*zptq*mxL7VWnvk?#&<4}v4d`eZtPoZ7iekA56)z>zMqd^dj^<mu%U({98F$O&
z>dZLYT(vH2MLr=r_NHqPu^+0hwwkq86W$Jf1W+$+6sj}mi?VsMIPIZPXY|JQ8ovjP
z=iuT!(&P5v6K?N~2mT5;E`|Ak$L*4okCb8r?bT%$J`viRDpsJO6}9}N_@u+VOzE=Y
z{P}aln}R;Mt4{lf^|&!aHZ(X32h?g`+wht#G1A3g+~~XeW_1cq$nV$TAVA$9_-wfV
ztO9dg9jJ!<4S_7i5gIJucH8UxKLb02hx0#CrR%h}@TY_LbWjWd{C7CWp+}K_=oGfJ
zxA_Egoq2`Bg*yrxP8T*7HWfA(wiLD&wiON(b`}mkS2*;1;jY5ng?kG37VazDUwBdB
zfx?Rm!-eC|7fztTgZTfU!o!7A&lOG=&J-RgJX(19^M%KsFI*@*QFwCn#=<xnyrSS1
zCJMPizP<di$0!vhpDRoi3hm9R#>N|78F2u4l;0lY2Ya1CH$Ppi6v^}l^Yv;iU!AGo
z)22H$Ucd+quM6{Z^2JMo@RDWzQ+b}9=ecj+k^tMgN`XI_qs2IVv-o=TweUmq7T-zd
z;m4<oFiu$i)#ScbjMErdgy(%5{&@02_L+lwpS`6s>b$;lu2Xm=ee|UM(e3I-Z|JmF
zaUbzRk5xXtG-%<gOB4K*`Yo9<bv)rb+H1WU;sOj6<CEY(j16mrAB=poMtoFK8m-|W
zDHx7~4<p=!Q){m}Q-`PU@*~Au6H@It2Y<A5unuq4?#EcvthYp@m*+_5n!)KjTpe|a
zEOm-y?C?8<A8fVP@E|unStXAsBC+KS{vNo>G5*cD6#^~8ScvEmgpL=d2E|9$Hp$&W
zspsjZ_^2;e&3oC2ay19N^qqtQg@2|eEyZOZkbxU_;WW1<^tVB`R>ZfAq0uR{2?NG!
zIU1rt;#%~=dbLyd(e{dwYOW<>XVI?kfz$BK_<Z^KYVJJ3WPW{sKU{Gf7(4{OD=dMW
zdA<eyU)Om}r|?m@d<ve)Zu}2clKRMT@Fy4uh9_XBh!{O5hlru!b)I|pJNWPU=bvkK
zCJH}?NL_r>N8A04rrRXp5tWJ2HK4f~1CCpsQa<$BN6vYdir$P<opi>+*PIyybIv)h
z;Z7r1fDX@lxk9D*6v7t~DV&6>cI(b?dCEha<}>n~#>m=S!L6Y)>{q@KvIZX?NdEX>
z^2djwA8+?*Q=C0rd>R77eMF=UzUHv<vDd!sc)dCe(WWSD!aGj0>Wm?ZLR{lKDtuEA
z2Q}uE>kfsoBG;YD$6h--;^m6so5&;@d!1vLEKQTip7Ib}<yoWX%fedc{m#1-;`q*f
zL{$_Y10>~v*WNaQL0g0&Zn@Z;g}`}qOUOI#7u>DT!56FxFx=PW^BA~c`xv_6=PJBj
zd-I6r)xwA~P)USI?+u-a_KL8>6n+|(URs5TEP9)G6m}hcv`#)!IuSqHD4*%Qgqv)Y
zO``pUA73XQ2L={sfE(PTHwaY04KjLzKquT_x85Kyer|BPY%oVR;I>1uZNP8PA-KUo
zy+L5t+~7{VL15wB;9k8!V5QvP0og#eS8j4tHVN1a43Qh0kPSjp3QUq)j3%^*LUSge
zMHHK{gceb79t&F_S{$!qLe?pK2)|8xO&23rd$kyAgCVWGyfl#&;a_W_-N#c|8c8~Z
z546|OETH6GK8O%Oq%H9Fa+x-0_t)xGOg$P8Vs3nt=KG!YJ`4&6@XEOV=#@T+$ltHz
zaAw@Cdv2%EXs_e&0N*OcDlvh;*Y_bIP=L<_$xh+!E;GCW#um4|?2=oC>*oO)UtNNq
z#F%_m5a_h8K^CcV`VAclAxkyV4?>ppeme*kOc60#z;p$1Hnl~lS!()imm?WN9uo73
z@crd=go>E1sh?fjtmfTW^^=w2lkv&vX6JP9D#G00R|HPgoQp{rQcEKIS<ZiBRvb3j
z#-Rl!7n}kT_*GoW@oag9@O^ghnc&&8&tOn~wiCYVOnI0hUT*qxt;yEYPtT5HeqL!n
z=MaeIUCaG8Bz5T(kht)H@ck7;-?xY0!@~C!-X8ppNrFfYV0MJih9;Zsm96Gv_O5KB
zI2E!%;d=PS>by7UVv-8eM06oFv5oQvlSnTSUzju0k>bKc7pOiLzS~c2rmGZ}AfG~W
zP5JDGX1zEKv#vI2f`{bCBw|3!X~!EiN=ZE*5?L+cP)-~`{@`<(d4zy(C?IE0ro2I@
zcG$kpc_>$Vy=*4?ML@8<mgcZz+YL%86vVvv$@naAx>zY9yqxk54#7&1tenM<p>lCz
zh|T}t;N3$M5oRI9MdWxILw-<5otE=@m>&jT)M>xs%D<pCn@8#edZP@i{uN%RrFFsg
zj!z&}Ny*9Z(VB}GHf7RBY8lhKP(fP&rAV*Q90XG=cnOcvOHXt{I(-P+$34tXs#A)s
zh)>afqn~aN763N_jW@?JNuN^fV{Hxg;UYuGc_&PNYO@l_)fFU-kx}JL>gm(uA^Q0<
zbe|w!@@z*uSUv4lDBso)*+yx?D=<l{6>}G7JI{-DYyH2+i&GWK6+OYPZ;E~lQ=Ni~
z<A<2<hU(eq&pt5Yz;EMuuLdI;T@pMm{7~Ut!SC#={hSYVc4^n`_2h%cy{Dkt!0Stv
zSBZ!GMM`JtYa*csr$@;j$<QkL6p0nDjwG0*(TBWKeH{8gj$VGcssU{`pK75qsxWL&
zJ|1^D;%i^?)QBgs*rYb`6Ef`ER|bAg_{ytGE`0wAk*cSO3FV!x0W_FilF#dStx!E{
z#6**_Yr=7ss^r)S<JICp;NDNXc!gMgxT?LBJ|~aZ?h`F|W?T9?-V^`OlMV8Tl1&dE
z`!9;Druf3}$tv-ppSr}5L{y0&a~a483-v0U@FCBY!4;;?n1^+g_xx$J9LMet-&!ZW
z3R#Luvp89#H1+aU<zfXH3`v>U;p|<f^leqd72@?4650HDFnF?TIa;LX><P8ZpH88<
zr+n%&UiZRDe)s}&hul57tDgV}o)z9yXopg;rh_bMys57@WET7`a!o*_&7#p0B+aBD
zv!W>#fx#b^_imCokd2vw9~y_hKqpta(k7vfX~Z|;(fCX;PhC>J%d(0A1{qPx3luAu
zpW-V5jJbu#w$s7_m<pcs3*uPu$<>QuZ6<~VKSwAAH-}tawH|xEUc5h3N201SMWZAF
zggzw4i%;Vfw;ZxS>9x?mt`oTxVco$X6+EL9T=+W@aKZ0ba6pVS4k7E!apZ(CiYpI!
z4T@K$!r_2&cERhEv<rWKbK!f#-``SrYw&yU6*S+^1$+&(pSbd|Eg004Ea?z3%k({+
z_TvjiU*=mS^_N^Zbw6_}X+C=8Gy{!L(pM6G*@6)c(#Mka`8aLkby+gNA=rgjck<Cw
z!V+Ph7y+>A<_BJ(VQm)AFj;Aiw`vG;Jj9$LHV;C5PBu2+!mwILqfYEqM4%!QjFlY`
z0;nxE3gR4JAPt-^(!$JhApw3&A#BY<J`sKaxwG~r24FB4#{8wmjbxRyT!U4-`gnCh
zOld?GFQmtPSD_`p3ahN>qVmzWSY~Qp{i`35@^GeA^+bUV{0@%HT9d?pmwG<@QhrAE
zJ<g%OA|QXO@P7P^M3m1dpC8g;Wze_710&%?>(4ZCit<3NKFIQh-o*C~L|lKybOjG`
zVkZ?7`mUKE)|1b8O^zHaMGuroT_0H+Ksbz9<(i{qgm{#dqc6c6O!jq}<xbb&6zP3T
zCa=>U6&oDx(c0F{%HwLKJS%3R0Sy{8U_9;ZN~5|rjS<&xK@Zh0pmyV!2Q+8LgLii+
z@6u;9kH_H95>r2{Xu5R-V7!QYgVGey1tz3eLJSuRNSIAZ3m*L%m!{~!M&*HAK3Pxe
zA>s7ML1Z6Q6{pn2gs=0`w;=2arhX0vK)2>00eb9mZ2;MNjAnbBXZjrcSI6R4z2fkP
zgXkG$1fv5(j}P8<`vXtj^31aXdtUkYg(ogtcw%U3ue1BYg<E#*-gB=rH00c}%bE5t
z>lNAQnF3~>4gz0Far?yjp6%;HyQ2u5Cos`+W(rtLc9F=g%{shr=3o&zN6>ruLFchz
zx$I1MP7PTP(4d@m1p_XAahuMJ8@42xFq2kUw5d2#b$3FvCKC~vYF`MaR`lR4D9bG#
z2}1m4Wp|Ul3SA}d6e|uVPx?Z2u_BLx(et1=KyM!<6RtG(3ig@%v%)f|#a;*k<$9NE
z&i&4$TW)xJ9kM>6z^zV_>ER(3O7M)0j-SX+NioOB*rJWe%IMnzBRdKk1Erli_c~-%
zBh}mpEkKEn2vmXUCA1N9pxV9R-vXU``8jkrQgvpqtUzn<4g_}Sdmx?sB0Tf0nbXxf
zA3S#MST;;4?R+4l3_TLh#3k6c`Q{{+)y5Ap-=eK5L&#eWX{@H_OF(`B=1Y}CuzB(Z
zKFU<<0818}3-ZNDXCOc#uy3|;ADapmJ^Ws<`^kab&h3eC;^W<W1_p22vwM#q`>cbt
zR?qJ*^U#a*G(t3PD|<BREHyOWX!@2&$|SpZ{0WI3OMblCLeYkAWb)xuwW$CgEHLs=
zDqMNS-$+=Us5aRcmG2~z7?4eZUPKt99}G5VBpcX$p|X3As%c?kph8p!#UBG~hO9|a
z9)ZNoUinH#Wt{)?SyhSBTT$_u<{%{{2I#9Kb!r<CX|UF66b6*nqiu<sXm{MTOZ~Dq
zA4H$s#YqB*sS9x)Kk!79v}QK+F8!yyuZ!@kFg$MVM6~4V)~n<jk72=1h)<xX?qQF!
zi+me#NRJ}4NMQOt)D?*0oW_9K1N}Dx5F&39H^58U(#`U(u%RzBeap=WE-<dnc~qfL
zi+M+&B&4AH)W9C+{`>EDcJDT&F1W@hMUufIJ~W9DN04psVknJ4Y&me1@II5G_u<ou
zQ^E=y>Da+!5JfC*ncown0u_hdYOsNFvVlv^{V#Ix4_KvPAkuG?AqKri)k+EVQQXhC
zM@x3kiO>ZjDuVJNlriPNy^&JLVY-*9#k@ncIN5ruBHW}He=)k!TsGuW`C=4?`Zl%m
zLS^S1?5XcGFnoIh`}R87{cQbgxqeikv_v$}X@#LEhqWxHlIXrxB)Vlh&Bc{eO@XKi
zF<1jhG8tU*#Di0LsLKih*Y?$ejwIm+&|Hie=7D73>j$^|T*){GK{K=97jq)FQ5V6T
zNObw7e6-k*_p=vJzZ#vRkR}xQdNF^XY6VfOAR@|2HK;f(U4J$5CDAX1TPYy4rCLvX
zsZ0Zkp!(1_r6usWoJSysDi)V`hSt6}sDRAnD}$v5MV^=HgB7niRH;o5`E6f}9C_pL
z5P-@~PnWYfViJv|{(==NH60hQxi<JhKO;lg!mBR~Aj@`PPe++N2F>zC3>#R?;dYNx
zyC;-(Avp{?R0k<R3Ya)R;dbOv$)!Bg1pO0mwJuno@$Ci{^BkU2Hn2Z<L`y=vG9Er#
z9XxAMZtnT;^9{l0)S3KZM49&55k8fZ*rtoM2Usd{?m+@<YtT$glY`ZwDsqS0k3Vwq
zq;q=s)G_D2W2c56I{D(^k&$!9#>QUM30c@I-}cw5u>3uZdG5*K(<dGoK7m(Ho_uk+
zNOiyRwL-pdiR`pAMcOHl3P=Ziyfi4PHT|SKe}Zyv*qWGN`&sqt_yOD;m*H_GB#Ikb
z(-l7-?=Lh`lgpUbBDF2PWIMmvtk%ljB@dY`uIqv4LB5{2*u)Lu6MON@rzs|!obtQ6
zQsq4VMt)5^qFezI@JJ?#w@^q8D;~!RqA2miWD|9Th$E@&qVv*sKz!l!$k*RWWiP1x
zz|0_M;b0*Ou2W10ID3#)D(bR=wsUJp!MOm5{pF*_Pr{TcdrzR!<Kh#~v?yv8rP^LY
zj^ax5XId3x4p6g1y+XFQ)j@icoVZB;iEs;}5AvzucV0l6qglN`p)f5oqIi<h*3Vpk
zhwx@7C4_q3XD*<FP=MB;r2Mnb(C|-xJuAQH0_t6n+NYYwXF}Sf6%=6xOOE%Gt?!LK
zg#)4VPxzsvcq__V@>ihDPD22=g#lEuK|xlU9v9wFVAJwVfb*-;VG9!dOoE^Jc`DU7
zFnJNsz&QN&@YiAr6kAWajvP5Z5GA#6@Th(()?vn!!18IzHYl0)SDV_~<*Rj%s;8Rc
z1<!>K4_{obwm<-bq{QeFK~i}V6yS1qQ69`x>lcaG=(X`zsCb_0epyf3<hS?_q>SyW
zpS-!_iubRV&Fbht7?x1Wh#WMR|DJep{g=h^jcB_PCg!K;;KET)u#6W}u>!(%0S=T<
zxYr$B()6jXp*Lhl=AOEPKAd(hA`QiEftKp=8}=IbeR(65beBCbVupFc(ug`lu19At
zRwl)cg-DG<z>`=X!ivuFYq0m_rZWhOAgPCb9xev3$@-NIYfcp#RNRRKXtmYo1OjXb
zzDD<s{lO!}<p?!x2_7VRw~%qEmiCHZjh!uPb3mX1@laz@EW@R>eBIFdsN81Fk+VU<
zbNOxeXTNLb?XQpn&Kl9m4+zm>DZI{Q`FNvvOnK8Ve4@xI7_Nu`@f}#j)FP{(QQu|C
zha%EzstcOya@eCWj)H%&q5{)aM!^lV@Bnw!<#9h%^;3rVqBjdG&-3!*a%6R<DlSPW
zeydJG8m!b~@5pi*;mgRSwpUX63}HAdsJ(&~hAF8<<<h*|K`Es=5{;{y*c;;J$3;a5
zB9B0nwK>0<1LBNdI;bp9ZOe%j_i?PG2adpRBek_PSF3SSS6_4wzG1y+(@im(=;_?5
zK?6jNtts$cy@-{!=L<V&9NkE7@&jQ{v{Ng1ww;oCwQ-b<R9woQU@ubcBC1px1x3J{
zxeJfe2;QnZ)q;+(sF@#cVwCU;Lc&Ue_Ga_~bW5*XLm*Vi`z0eC(bd{3!-ry#cVqZx
z&=z6U>C!25M-c$X$9eA(WpFX5VN-)w$)fv(&9=#A{x=2-%k3rmWwW)inMlhge$T}T
z%JWi9WazN6l0?5GfrhkWB#{Uu3*aWB&$r5Vx%eV*#m|R=Y?n<?pMrdLeR{l%F&qPj
z{2oPT4a82|2}=J&@mrubg-xz&>O`Nfli$M&iS1RsZ+;N4NpY`}?I;={BSFVStboc<
zg(F4(VKXiRLB9@o+9GKQ@i7<U4I1l)T8;V;Hl+;}irAon1=HXaMPngderJ97PVhj!
z<;X9>Tq4|h<8KP8Vs%(5ydP%8mKrHhppql;xiHf8G{i((wO4YFK&*gNyfD?UQS#b}
zm|_YdG?oU_P-XV=6ao_JG7(0?`AWb4SX5t$!YcXdYX6ytXwW+<RqT0_le10g<AfPn
zVWx07_@2^58a~U)7+RXfA999Ts^HIXHd(6TuW+tiszv^KD)Ltye<@@d(LbBfKU?^-
zbcy~6=4+c}n$({~3qz0;X}d|_s%gtf_&c_sgui23O8EQBLyv_`E5WDg?k0AX6M>^#
zPn*$@(5X_f8*NJvi<E)uUW2DR3#Wkz^#}Zi985RJ1}KO^LI^zwi=4sxwBO2qzgBKx
zl!_HcY?9B#u!{i=)B5&Gv3&vyEX9x%?Z1hTo%S%(#HuDub0S3*VpW^w1lZ^6)-PiC
zDkHWE-|e<}t#Br6vQ?1C(<H?o8?j%M_8iG^kEy6lxGqq((23-cErWVxlWofP<f|yf
zq0K)OsVVGk6m58M8G)lnawsG=iO2ru<Nn;MQ<@~V8Gyhf0;kcKMfrv(iVL3WTkZ;v
zhA;CMP!x}_6q`{w%$5lmRz&c;0g!nSPPSMpSQPQBUo;#NX9WZSAXk!#<#0v94dQim
z!(hZhSvWkF%ZnU4EqSKXUh=FG=xi2*#JECB+2a_BT1~Gr`Wpc|*uoY5dMf;t0w>fm
z1lbo_$6e)?XWZEa!VwqQ64dI4yS!EpipG1z-YIND4xRv4Z0bVkFajvr3g^Gz@73zG
zHxF_?5ZkT9rZ4_WCJkvzTHrKjn_0-`acS5amECf*;ipC!2c<7=RiBIfj6C7NY|4+P
z)CR#U=rljOL4D?%63q)IDOOSgU2Lxlm}_s1ZskQ^^>^}0<Jjakp&eRSxGU1Q7QuI2
zxYO(s7a@h@3$umShhKHQ@>P?V_`5S6C7%5)l;JzrzUl8CBkbUEr;sy1R7J_e5b?tE
zg*S$T!xw_w1Gbz|xQdP(O3^~G`x?&9iQ*pwaidFUA)E4b{wEyX(*pN0WQAzk@9Fk(
zgk)S>zSghW2tQbU)_)Z4$O=vjD6FK4>GleY(Ok3q7Fgs8OonLZ(E&^W{9o_HkO|PR
za|@jCQ_2_wdxvDhL-N-<<gW+eh5gs|?^AxKEziN{^ljm0ly;w(aENVKlFVK5*TeWV
z+||-v20w?rS@QASAWUNb6(VE8(<e9j$TsSiAMCspY7a~4sbBW{TI9KBV=&hly*k(=
z8te}r1oIV?;d$8KyCm2WCm$AR(u80f&w?`}LTWe?VsvR(U=w`I6)kALJ@)M-_ma!=
zZDPaU>>7R(xorA;bL=e+z&fMXXx~pe4)H>qE$m~)$A!k|lES9ZyISo{{v?6iH7t<J
zi{Pa;L_Wu=0z46_^?fnmD9DdBX@P|L-#~k<$oXPE3ztPz)a|P=he3fWCdGcnAEPez
zxr@I=fgb8eq1ETxn{((~7g0Qd$vbW1r=4f$)X(9h1r%{pB*@1qI6;zrW@cuP<)$p|
z5Q5Ypv}_E)fzliJek40ldry64b$v3Y(7?(9pa0Orthg~)^qP}`1`Rj#KDCa#zgBo+
zx$aVW3x>+}TJ$C{Wf;FqQ4pz%vJ4njqMxo0vNrJGleCO+5l#CFF7_XYWG{Sady9ya
zaTEdE3QP%KMNTab`8TSS$idExC;Sa1g=^aL?Kx<#2^MjHEb1}E><9a-sWN&zD2;Bl
zS75eI1viXi*jx8?P7HtXS9bl>-oF`_U3fo#_>13q|BuKI5AcV-@xj(x<cA0O!{7aZ
z4}UK7F@D3(9-SEtKl9st;degt^WkTQ_}xFe@5uh}v%~&pfB*b<g`eHQpMB{M4*o;<
z*`55^A33l2VEEZx{%7y`(?1D6yPH4z^0N27JlWw1>A(EJSASo`w}?;w<iInB18>P6
z9J=O9syh-Ner03lsN(SGv)k_7vGPdh%jlDb4{dtin?j~oz3;tn_@;X!{*hX~_rkAz
zyi>Yi=~Y+JUw?ehHGj1lhmoM>m{z?iIZBNdzQ4W7-z9>@3EF+k_37xPaFN%L6MGvr
zyV8#25a#`zw<s?9ApI2#33t;U{-A*0R2Qqhvrm2JfcnQ_^^ZH~kHn!xB_`fi96O!`
z?A)bNJEZ;*;rdSTBphczAQ(H|(kbnL`tW0?_yN=RSN2h0?%|_S0soMG+H0_kNZBur
z+yq4mMrmh?-*gg9+e>{l3!RqE;3IN{D=BEiCS=s>(Vy3HZsJsFGxm9lAQI#KbuFcL
zQgXJ_{_Hb5X@uT+kF%4#8_Je;`d*km#9o}i{rl*{X{4MfkfKj%3>><gm2Xg2pDxm|
z79s#b8%Y-%fN4`(l<>i4K{7nmM@*@Ru;2C|HLgtfaSy^0+6wH|sf8paTo@SPohtMU
z4tnRa2uRPePD=0cf4&<(Pi<LUdJle%u6uzf;U!5*_{<ts!iE=!63!(lVf_kLLf;ES
z31dl0`0#bCgv<*>3FniP@cJFB1e~xX!<PTRj`5KsCH%uOR>H~`h!P%6Qo?i}D`CwG
zL<x^2DdF?J64t&zl<?9dB@C`)OF*Feg7671OH#s)7g(_L@+2kn`@z!s7l<W1o}`2u
zHgk7)q0AjFBq_nYhLy1N1!4(L2qi@4PoG-KI#||fl%7m@{?R4;{AxTO7tg!QNk{*a
zG9_Jt)~{fdcWHj=ohy{~t6m@oxO3~|(Tt*#><dIE6ReZcU;014hM%WCzq<5w{2c9n
zfhd7;ama&rN%s_&Dt3XM+G;No$zhRZ6}cjjMy6Vi#vpCqLyGQN(E>?2|6J*nb$7<!
z=ZcjeB*JhkG*a7CDE2S-^7hj6NV-s(Z<jcm8p(L<aX5~%M4aQ*I?lY!JEw7=D|TF*
z_L|5-T@)wXAtzP(HocwpCDbm8^Kd$)f5mI92^^WL6s=K;>zbmdK=Th?h9t;RU(3r)
z_-zG_KahV=WffBFqRq;%qz-S4VhN$W9Oqh6+fWy+36Vo8eG^~JS`o~n%;Deg2^6v9
zT8MIBtXjTA;*S(3MfSg0gw)4Sqa_M}9p0o8IZQ383mXEZ<@oB-Hv9vflvdy;skRS4
z!PaX2v6g@I^N$VuW0UwXy3TEtw&2&&R{R4wDNQtwoHrcB`Xh?UNh$%WiY_087{WVG
zTjqYZ_-Y&O`(xZwerF!;Z$F2_ck3|VCidS5&auUU?6Pr>a|W4C+J#LZ(us>vc#Pg;
z$?mY0Y<)~JO4`gL*^YB`0udknhziOoZJqQ_P>c#PS3~Cem^I%xPdH*vlxLZT?+wZ!
zmZ$i<#VEf?4VcgK)_h(Y<C9V$^YFRxWOYg$+1XXGi7lAZoHeI7;5(vG%2&+8>7Fj9
zWn!DNV&QHPMM!j%Y1ms0-eali)>5sANks{Wd8Bf}Q#z??7N>O1EzJDQS%%kG%dj#g
z1LY6qks)^sYpm+YqW3uBuv6C=gVVvDd^&)XHUyq>E3@Jc-pqXrh1S?8Lx<pt4S{=|
zQ&_kb`{*BTi5IasCb9D@$~Uqm-)gPNzL+K{2{Df*4<B)Hr-4(<;{?KB`^{OLl#nad
zFen{GrFpeNPCh(7f8gxV;XP4%x1_<+zQ<bHRWWHP+c1x`>--oh7^|XkXiu5L4_b4$
zCdMJ90_Ndx_>5AivsYMV4*I9Sk4ldSA~Nat1qKcZYOt7xt<lp_nHGQ^YvB{tT38;_
z0_6hc(ZVf9!?S+HK>SDr*FSjmOGP@mBD}OA;&ju!!m@nYT9(x@S!hZ>k1QtFP(&m$
zd6DJ%BV)OuV<IIW=8<M4O;fl~E5fYuIrH+hE98Ze9rN(ANgVftU17pTPSi!AYSjFb
zn=ps}9Ock<=EEus=6i_!De*E7=l7mOsjoa%e6RB$b}^!+Xo~vxaJ`NaQz6qd_VhSs
z#X>*kf?_*MEY`RpO00)f%ObNLTRpU+Gr+6;o}ws*vM=)}X1%YNZZUVla~9&}o(OS`
zE(E1Z<`Lq)WBzVjLKD@T6Yc~~D!|d?=L;To`w3*nsM?S!CgIp4_tQk}_sU4IYVM4x
zrgR(r#;kTP{}|*S`}oHJ{&7hB7+n&0yF2hVESs_<^T<9R`XpI$Q1Hp~bt;&{p-ELb
zEeWTA;uOJ%`$+#9%XC~Y6wY;RZPs^x-+)}Li551ASFtsT4kDD99g!&EyalBPcxF#i
z?UI-!)6}#iW|cJAD~Wj;O{Yp?CPdSfl9*{wh+h)XIR(lk5#v&zR}wKA1wkbdzfiDI
z5^(|zxFs=G(r{H0BMmutktQyYdld=c61g$<In<F$qLWc?C<#j?@EJFuG8|~?R|kr;
z6{nt7O|;5ok58Hi6IQX*jL1=~q_Zf-i+r;~QU+%L1@7YDTikS#_Y-03X}cmFWP$j_
z#j#bj!rb2x)@*ibCbC3n6JDy7m6nmJM5d=2wVxE39x5<{<swysa!zc9nQYauMTP3b
zkU|rCBSfwX$t+3<l{Vuae`HuGCE_nH8GpG?DM6J?F)t{Vp2UwtSW3CBPl{chlq%gG
z2>tIEp_2ospJUqt&XdBqFu_4$(Vjh5LuUT1_#(5cTS_e2*JZ@{2H-LaF0;gCHsn&^
z^h9T1bnWO89Plf$cdr5~%+#BWnes`zJ|yu*LBax*k|id)j$Ni(OuTy>>^;P$V4O0C
z1Qs^7@~Izms1M4KV-tk5FR@yF)>tiRvD%gKnENa<pN%q~4VYJ9O9JkvjXBhS`zcEj
zZN)#bx_+EZpj+>=1$q+#31_T2*~9_DUAkRz6XxoR#`2{FadU?HS%W!C?rNh~>z(cM
z#tdng{hlp}&*DWDEYBeO&s?-4<0sSNzHd&eB-wd`kZ9pO_wWdvO;e>kVpw6K5@T@?
zVjd@M(OFuNTt3Hc;v7r#KO8;7TV&KhwY>oJSZ969do>Om!8j1eoOLk{;2auk_Q0u*
zG&Hcx_`&NJS-KG_OEQzs31wFNPJ#t5H|AZl;H84ZA`TbVAH@xg*y<wYBiQOg;?gZF
zjwp?fU_5tIhHtO23etL&8!|Y2i8#$-mJBXKPP1IZ&uefrkYAlNBJ43{Ov7fEEeYA3
zGYs!S4+UG#;}|>~FpHyz=v+vF9X5x=vIJdQF?2667B4My+cHR97Apcc;!hwZ_*45+
z(wK1Cm?sUjQCku{N9_g?J>%$Lc%rfZPpp?VVdHf7U!?EN)0PpuWGrf01aIUR8V7d~
zuL8&juMr%t(Z}|NF=rZ%HCqxr$8p(7ca~12iU1aT$jJPVG4pAWS)ReEg7obX2$t#)
zyu+9U4T87ZlIS@E>#0VDMsrakL&s=G(MWjC!2GhYNNIuDkeO6?!hK8NDT2|pI!2!`
z=0?Nl7i>xN9HX5_3s`WCr3r-F^Y^7=4}PQzrFnr-{BvWW)1sKk;J^!T9Zeeu2PnCY
zz`9I_^{dA0X|R69mPF5Cz20A|q=~~kS)jR_GL(HdyT3Pipc^tiVifMrnF+djbh!@V
za%09c5SQAL=sAdMvDDJSMeY8@veAARf5<=$84HuP&#lYgL^PF~NdH=)L$lYI6%Cp_
zwj_EE&6ZOxj<UP#IMcZ0u!y7Q{nL9RuCiMrhU&braA~31lEF$2xXZ?viy*dAN9-lW
z9BGK1wj~iq%pyPY?J!-$^K}anl8bp#Jhth<%@0juDlckiQ8YoPT3~@9Hmm5;hOOpx
z#tKevHN}9ptje2WH3@Tw*BbMq+03hKN%S18-N)$8S-SXxXM>7baGi>r)ki&Z>bDrt
zpD-3bEusS%&*$HtAu9Z5tCegx>sLeIgh|Se8FQ#%{C-;!J;!*<a9&)UAP%9Xbx<7o
zpv@b*HDUxmV=P=+1h-~f!Cf}aozf{2W-OmJW=I3<H*HDu9I!Qy78`UZ?9&Me@7Obj
z=O2tkNej=~49eaVUKCUkCLDif%!h``|F$L3b5yQt<cb%I%`C3V#^qfYNx04?0i!uv
zGGg0T_G-FueMaRo8{<>KFJZ2+#h5D%zm2vedX8VL=S1Sz*fYlHMaH6}?Nv)N#XL?@
zmP-c-lZ$(ezg@%PE?W{k$6{Z&%`Cp=hvkIxIQ^$upDb5rM3!b0`*~t%HS^aQ(VVeD
z(jvM)BQCy7TG7J-1f6HZ5{V2B?h=4R3j7H(l~)+Es=@z+Es37Pzl%x;j^JP{tQj4p
z=|NqTA@0RpP&2&hn*e&+ml*2jjD=4N^)69nFd?|l<^=a*VJQpddHGhuw@;smzRs9G
z4d2(=lIS_Uw;e}?akh#3542T%Cm!cT{IJQ3jPnPKm5>(a+cR`KjW0!Poqn!pb(IeG
zPZ%?)f&F8)Bzg|^?#BwnT){z+W$}^-hQgwt;Lgy8=+?FeVpGVpZ!yfjXDohNn0IGp
zi2tnQKP(-};>>c~d73TadIX$$DTnLTI>etbW>ACp)3zjf4)Hef*XNF%d_+P!=}s4M
zzImkgc5lWY{<E=wX+hjBd~)3@w<LHzhYD<KblCpEm?aIizq2LLbJ#W>q051t)0Bft
zo=qmUV6d+5>(vZpQ)Ytr!g#6jrQozy$7#DUHyTb`Y)SMSr{yCk^}^BX(cTn(%6JSL
zv!Ax}tjOg3?^iHbr(^IUV-7S7?zJV+a}3st9bB~86c?lO8TJw1mhhZ$nKTwDEiM}}
z*szc<f+v(C`#dQK_3H@bjCs)zdW9{Co+Gqp7&~}sD4U$AI7st0YN%H#Uczm#mEdra
zpolWsj||>pMBikrg0zU<mT|Lz4736f$Uv#;SRtqyZmidle$JRj4e8g}lIS_ohfmUl
zcp{b*+rpLYw<7sQvkIz{#l>=74OPQjIvYjZk_KDAhm2K|)&lOxh|B(1wrq7WD`n%v
zB4rRn4KT?TmIbTUutB$m4;b^SS;J4*l89S_#hRpC*5s{379=DW<F>W<#6@sOP`ABI
z3ttd#b6$`-FqLi!x0QX}7{X+$rK7eJx0US*x0RLTUZuY@{#MQQ{-+?Zh{fkS!o5nz
za11#eF+rz1<-Hs(N>-KIo~t3dij}JtCQR0p-rf=JNusmTeI~QaWEcjlOR70vX3UcY
z$u+hlWJkP8km%ZDZ0ifQpYy1w0ChTaXNV~MLcM{$wQP=djNG8Hh$*`Zki(`+$)hOx
z3V2kIOW4VAyD?81as##`dX8Lw7-G|fu;_HrzNSBBoW_g=N{iEa8DIw-Dexqef}AyG
zMT6&*Es37Pb3G+_!W~%+{~SCNEIMaKjzyuGyD7tV*;vrDu-zb%J>fnopHtDxuuDLO
zO6x`wDm_}pY-teIZAqktaKP3%zKk&R17Z%5F>pBI>LEwe>+f}F&LOUwb@-xTIwqn}
z%;V}8Zh<qoXg?3V$G5@p$58|5;rFoh{IIc((^}6<h4uJ4;!qp1W`&NjLPyz9MOju6
z#p$dfZqVR5Y`n!9;!jn>OQ=|Rr?Etu;r)Oh(KSw5@S#>lKWD2oG2mjZJyoyH;0m!W
zVUpgE@%*H*^l9;wVNxw*GV3#`c=KzM0;P`|bERSOF<TPS8GOW+gq%0AWn9s`sYl`3
zEsxN>^0diUY>_$EDr2!1C$W&%#vyr0qf5G1*slK8Sa)gd>eft)I1e`WWIf^>*&A#y
z2j@wnO+rD`-x#x}8Q9ltNk{{m2U_dVX7891B+!77$9+<`JXahkd$wA{`K1-d^UuzW
zdJMBy8SBlfdo|U{W+FiZ9J?Y(w#JfVLrE03l2Amo!I(`A^L2v6;yL=8>8M5Q!U*ze
zF2x2oD2dL8qLY+xjB>=^r)bZR-D#|gwB2(jwq^S~^C0fhV9h*deV$dElHC6xW9~Gh
z_S=$>y>rl(gmnLGFTHgC);{9Kvp6`CvMb34YDevAzQ$}+1!IM!HL9C3=mo?r`oBE>
zJM8#M&DOfngpxham@&<sCIpGDJ%3=qW4v|easMC90fGcYq@!z38G$z%3z`;z{tVqm
zNC9`ikJ5=(Bz59981tej{d;Ul$WHtk#%-YwSlD!$7dM<b2A?9G9!xhlM<7aW!Z`i3
zv0!O&+MEeyP!Y}|U?t?Ye#)334XhuxB@qY9!d*&_%l*`Xgyf=EQQ1vla+{I?<j4@n
zb`8YlZOxYSKa91W){<@t)7+R02)`@X4V|t!txl+u{bOUsG>iI@Ako$5F1X9RbIcR>
zT|2Fsm;eR&8ZqB+hFi@lt^40g2X>;~s*JOj>-0(N=rn684CvKsdKFCHnZXe&l$+1C
zYGRrccnY3R(e>UWpLj&#k*62pDb8bMlBfU9l|%h4TN1L{e#4f8bRKMQy>uS7`NKhV
zMmOVPh8|3MIGcDnatlmsBr<)O4e?;l3~?wjt0QSbr@~W)t|b@iMiVNw_Zc&)d77*(
z32A=wj2gDUAIAM1)m9xRa8W&t8vTYAz=uMMl8c}UBN~ZV(qJ=qsj+I(4ls9MHO`lf
z>TFcmLVMtht|++(WmDWyLP_@{#{6p5aL$%Q+!`!AiF8K1_-;W$a`C6OB>==la9`2l
zm0ZkopOG3}mj=V?itjf@E7?Hla;C)Uio;3O6>l;AR?W=Xf<)KwX5pulTVtbybE)AR
zJ$*VhT6As2{@`bf1y8B&_BKpi=$y&0xGH<8k<C>q%Iv;BDg5}LF-IB{KWR%s_O<`Y
zXfE_bb330UMjJJ7mS5EoyV(*@o1-1$_6Noyrp0ZCf8rQz#z6E~%?chh<Pv6`zi-Tw
zhTQMkl1Po5b%t#t4r)P)7UeKGQ7x%=wZs+-*S{Lel@_i|bVN(Q7bSG!T`Zx#;$Mup
z(SZ6#L1GcT-H>vVp5srwaSh$sYI(L&ou<rZ?2H<9d}$lM&9L3Dc4203+F;ZkLS8+*
z88GXgirv5(NFYwvBX{~ouSs&7JB(S?Fx_fPLUz7Qg2aMP<zLnXe7?g9SAik1ihJC=
zdac?h(p`(9w&E<7DXMu~e097kBJP3X2)&C~7sYX^l?GLgHDZ@s_3(SxppF=;IISOe
ztcyVfDhi9B0~Lwl3aCt|h?ZY5DZvk+p)3w`^))2(LS%ReUjG4Oku<}*&z3~o@GQb>
z8KJXFvmhb4=uy<L_54g#T`G0XOq8%IBOd8>YsIG5FjjI})5H0{QU0=3pTBT!*Pm1)
zRx{>Evz%#L5<SOk+bO!%37%*~F4n^};nJDNz|_4NqxKGC0n?(kJwq2y@l-u5Ctwa0
z*b*wN-fqm22HW@BlIS^XHy&@*5l-Qt1QoVfTw^-z;@;aRc=I)82tQ#gY+49!%HV<;
z;#PGIxPvN-6)eH60%Stf%r6)-rUCg;TN0^(w2s%uC_qO|CQe*KN6dRow^-iG6D}&i
zP{J?7jyzQjN;<`B^B<4WlW$;~`KqyA)7s2fh9(H8!^BB$=>Du1p5xJ4LPRa`&2seC
z+-I!xCmpT&6=PvEJNvRAv53=+t>>{Sbn5g&W9Ng@_8nY|QLIEAK-X4`;?i}!nm%C2
z#VX!(1y^+ir|oB%Kc&krPnstzG3H4F<KJxYl%4)x8PA1IuWTBdZQ$ZEnxkVWKi|U6
z)oKi~<R*;Q9%J34?RA?ojgT{V=YX>aSP8N9E@Or?urjtJdJfj@XX~D*YQU7u!&E-~
zO(jNeGPI|S)sPn2+cUVVM9KjVn&ui4ILgt4%*?1Un;PH`+LDL^ZZQ**lPPxC79=DW
zJ(^63OS6tXbbR>SzWu_tJ6K1Ly7lBHY)G#%R&QEE;(N8QX~eDX+s{W`5od~zNT`K-
zr7=62!OYr{h(l$8wlo-CKCvJnxp>|-t?93iiMu|#t&eqIzp;)`Z$IbdTJ<_=&EoOr
zJ<gd+IJ~Z&D^RF<sp!o_477BAxLo!zWAKvAkz`L?E*nW&F8he_w`x}RbArSoMs)qd
zPD7m1>G;{!h)Ub~WA5)?G!`|bg4^qHN(FNi<bP#fU!N4ZecqTAP3ymBOG5Ut&oFQc
z9lC8B#vX>t$U7nhE{<(ThN*iqhU?qL0;Yv)d&aE*D22l$*b;)cZyK|t!S>I#Bzg|p
z{RrZaVi$YFPkS?1kHHPP4d*dBHe{F*?Ks;nkL*d{=%Q=Z(Jpv}wAcW4_AktwOLK_r
z7g3<+%Zi!t3~g9}tiiSj7zW9T=-!t#HhWe(cS7WMlQG|#m0WL2qUTmJa7N_Rs0LoF
zop!T_Sbi=2YmEH~V+Ew`{ChH0k!Pc-`35bebVFIGZdjj`^Byr~QG<EdmP8z8i(pH}
zYdn5ikdRz_Xzr23I<8uO!mA*95~o(3q6_U}W|i=q?dP(wX4Bda&KL`M;wuzFLmA!R
z`Vh;*URuVCXjW1eBo;Bgx=GRca30}n13TT2TJ^V9#4wn*H6!v)V@0Gz#8DA(CBI?0
zlJwUbl7g`xFy>5ysAEe)cA@WMl)D9E)?H}dV|6SAVeWCJRd<dT>o`DqZg`_3x%ul1
z_a}`tk{0g$nHiP{X9?7^lO$1A>5>TspyHnp)qUKURSo}-F?ii(oC{t>v6l1MbnL{5
z;dq^3{2>GKr^ZsI1!P@@v)AAzJB4C{h`N(H+@Bb;qAC23Y)Qxt_eEP0a^z&#uW01t
zZf`+Ca>3(+1qrDO9=|O}`1zJG<Q%#VP+hIx+NbR}SIkd&qB7_tZtJXKRx*W>b=tHQ
z@m{5qVRmKr`h}THkfe#(m9eDk$}Ps<s#*HYf<#y4c28zmZ)}j&kr?wIs^I1$%$E3Y
zNvh5|Q_mMGZhh7{DQ+Ve!)XQ3eMIl=!*(>-Qyn)}TC%6oplaWtR9K>%8FM~GHku1m
zec9+_Nen9uU$!uDyEMt29W~}x1Np_aBxD!9pY5bu{9tPhVox0%>p|B%I&^gyt-#Ys
z0)Bm76!`g@GsYK<1x}0c&0;+@;5aKdrZWH)juki)!hwP@YZ{!MEs37Px%r6Oz=|pr
z8dG1!OxPKFbR>#lmo^OCTa86a3)~hQat_|I#2fWRS{83y%FAeCY0@&=n~nL=(0ZdS
ziJqgi{wU_W4LS-a8L5QljM9gVMM{g(1{9})C#o_{LMY+<xSuuVMMLPPZAtVTp<RTK
z00`Yk1SAhtXu?c|%jjPi25}}#d~{6v5@Y!#W8u?cxm#c<phQO#Rm8=Bd+LGciOjv?
zFcYc>zhKOu2Jq)>N%S1x-GDIIlc9J_0X3~b`Ir30;-A!SF~r|77C$Y-14_grvYozH
zWw2HL6^s)K48LW}p@#7{Y)SMS<HJFvDE8H(jGjt+odJMbtv6k)=#u+IssWI@<=F<F
zVO7hk$Mzdo(qQ~=+0diON&8S(S1M(r8xC1C(e){d5{QO#86lcF<R%=~b+a+Inl0Se
zoy6OhUbW;Z`tONOX)Wed_|eZlHi#cSt)3gjj#Hl36vOV(>Ltp5;?gzt=a-}5(p(K0
z-iNwN+eOH<-TS)CG`JK4aW3SN{q|)Q#e{N`<Hk&BC?2&X(R1)Noh()^QqoggdXO09
zCO2WgD#n7P1#B~JGZ38dT4O?tn{Z;wMPqg}s0y|udJa|ETuytgh78-=jD<`KTkQJL
zrAen<yw&*IHI&|LOQPo}4F?^H`cgg2w)taXvB4e9bsCf>YM^KrsiSCmCF(R6fF5K1
z^TsMmi}{hTt5PqOGW&yNF)SBQWR9hjR2Q6ZBF2Y}dDkrFXKhLJ++y~QU_;_1vG8<q
z%$clLryW-undeRv%h=h1BU2ifUQD#~!fvxy*$)2LSP^OMAe+hil4Pr8Onf0p))j~C
z`I2DEjv|Q?M<-l*_$6aDHORkUOQPqH4;^u5y~bBx{ezSR#Fi8wjs>cex%zfiyuCZ^
zyA1o)8+#QwXOWJAG!$ErC}P4?r)3B6TbY&}6sMr}^n^<jzhjHp>v7%-OZqKa5<Q3g
zu3&7?;1|(XTKw;bkdbyU9}!m1?w%O}T|mVlTht(kuWUMtSgT(^b&D~-nj^T`mPFhl
zEZ6;5@w`4|K|*qIqitruUwu5kxT}vFaPpT+sT0gEPI=8jH6P9Xlpe=Km6hWfLzFx!
zk%Wn>juZGQ-!Np&(UZpCs@c}#g2W=OI<Ce-W4#*eriyH=jy__=zS>yCl!|Sy$slf(
zoP@)9;&lkm8ndBk`e|Ddvj5C5SPQ-Mw*@<;ixmVvwD)~12<)S_-tN|jVfrt|!li|2
zE6%86?r7h87=kOsC)Xw&aq*vx8PWiIk1dJRfLYf?sGey$!85g!8Zc77Wh_-%q+)aD
zYm-*-{=4zFYp{Gukm%~Mx)!0>E{v}{Qmr--_q(;|%vSlFHT&1bvZd8*UuGiYLGe?G
zhYEeom<vtAe_=~P8vdWzl91I-jPn)68$I%uJ22~UCW>wYMF)Pq80L}ps*{sZ6Pc?a
z+sqA{7G_$b#SQK2M0rGnOFANmxm1iOVIg;iF;kieZM7vKO{hoU^&hQQ8-!fBm=~Lt
zqLAs28K@)10;TQT>od7HM+!U%%dig^v!cOspDl@=!?WcS9sPkg1hF@AixheTCN~OF
zw?+)rl(BGWq1qarzk@<J=1y$-l}UkXM-$dm^TrHmfVs9LdJfng0T@+Xi6wIE9E%*X
z|B$%jYMQomL@_jYk6~>as~|0`dl*(lhG3hZSa2(f%dS`irDrBAm_BdJq6YNqZAqjC
z+B)dma_q8K&lPE5oYH+~>(zu4&AK&W41dO0y0jQ>#a#(LcOq|xRdD7`K`dc$^n=C>
zX^8!#Akj4*UHH1)`vM%W<vgiG*BLl-a%>OzP^2`S<2V-ZBHflyNjO>M%1AM$zi+I<
zw3yx#Vwzm9E7J6s6hcg7>q*RPD5eT~QZ4WA8gs1y|2wuMWLNzyww;BZ(BCU9<cJ=o
zqT*{(^k58PL3*p{<qDPJQ>}Of+Z9n_`}h}QHKnzWd!>CO#JMCUC-XxwaS~Q2X2d`e
zs)PU0m~G8K{(oB%JvWe((m<SeJnXm)Ec_-|25!I2V@JRgpUx&mOYbrk_@d+b!jfha
zx?yv#W=RiA6N*RSS<LUG9u(8bvKsg>Hbo5)i%QrozQb4w&7!v2lIXcb9g!Br>p*g+
zPM49$;Zg#r;;0=h1VOf*BgRTg>wt!(_3-+TQaq46E^#%GB6GxO66WF$7&EUK&3(2c
z7Sm|r+q<sVXsijc(M%aD&BAEni_4VZP6%e^HkyPCLEe~o&1hU(5<NE>T+7CR{yomH
zDAYw!5-RZP9x9RaRKdKh8Sb{RxM|_0OWc@W1S3>ejCvw*7zJIWNtum^2S~^sJa5dJ
z2IlK+NyNdl$i~WqE>Dy!NJuU?Cul)Ja={Cp79=DWFTIlFHZJvfWO0|VH=e9ciBi3;
zyJt(`I`;1wTS{_2AQ2PSv7bs>$Nr4*w`wN-X+dHUXW{c|wVl|4-Ob+_3!GBT?bssR
zvZT51|2F=1P2K;?mW1p@f5nI`^xSuu*!UP*ybeBO?Qhw#Fw+Um$lJ?#YooF_y)0>Q
zdZRG|8UX#aBzg|OW?CI92QDW}$V4hVy0l?<?ll%IZRgk`*2Dtd5|TyBlGd#6GUi7^
z>#!|}o}+a(xR-}wsB-b5S1uN+)qI#QZ1BBTIFmh4TeK=X?^IeixQUKq$Bh!&ar+BT
zo^9k6#tKbqBQHU`Mp-dFGfZkEw3Y_nvW0Wi1C1!<p(5eL8c8^nXj#(nVow;0qM6sr
zZAtXpyf!~pY~Xx)te`kYX=4>GY0y^ly0l@SUuP^@TA;UNW&+-_Il)^j9$z+^FtvKE
zF+Unwud*c(N6R9Jm2of!Oco?07r$v61P~X9gCAYoICu}Po1*=Y&ZD@92U|T)xpY>q
z*VyY+m6Lnj;&e24D9wh^@h6RuOEyLlJTW?cby9TvapP~*OzvZXghh00y;!*IK^(hS
zua#XWSS&WUxUIWcaAU`IcW=hs|4)o%PpR?t_Dn%?DBLzUMD_(7hBNXNNzwBk8MCCp
z@kLuWY-`xIQA`%Q@yW@#4KJE=?cR*hxN7Ud1kRfM0}aZ1#W;jp28SvdNqBtw%JKN7
zEeYuh|Cu3O=-JHrVQ0c?Vym{}*07`vj}=8M;W<NfYtNwCfHSa}r)<bm1fzt-w4KJ>
zXnp1;TM}`MEQUNeYI4uDAR)Q<d~Yn}wqfU~Rxx)GNgi3uebSx6y7(0Kv^%F-<z^8z
zIykc=YC<M2vTZ$T3`JU}d^>DQNP&=A7JZjPDZ(2zNead25=?H)m`Tm#&f1dbIoRm-
zjp}66$<YczaT1_LF->^R&^==;Qd;Q9Ef7yxg(n4}g!RzN#=K|<wQNcB9HH%_UcG{2
zj798I!MYAg89h`B$Cu0JXvc{Ch_Q%i5!;cu$UO3-HNvBUT*9K{4;%BOA@@#O5<N$5
zV5B%XiQ9p2AdIgcf#E4E;&^rJd2LWx73~y_cDY@1y|!Dv#HjwdvG{3G-JQt?{AX2s
zrv?AnaG$H{J`?7-ziP~&2Jt6tN%S1z^%M?ISCN-UzOf?VIm7mKW0BIrwm}5If~TPP
zR7EIZUGOiBdC?I1pSC1=j!;&f#)aHswbm?77oX-kMv$kZ8xOt9GDjKhiH7eovg@|>
zYHBm6oXmwaUsV=b2xeJic2Pw=p~QH#F`pXhD{V={QMU+#WhBih2@4XEi;vh&+x+~~
zV~d-Aaz;E<Pt$>c&AK;#+UdD4?evf_V98#G<V{RFy(KB_blmt`HA_1xNLZwuZ1Wub
zy!c0J8LrTB)HCRhx$_r{MNO$-To@(JIu#`!F7{t}N<L#{QZ~agW<}Hbge?i#v&I>?
zh0bR5^MwRu=eXxKTd0DI!J|E9c;0L*P<nXyLV<F2GT<nTd{-u|+`iG66%C#@*pf&M
zk9E@ax+AS(S(Ny4@7b%Y^+lnYvn8YUv&M3!MeVxGgwH3==b=8OEWpX(b7j(6?oS&t
zrQ!Eeg2W;^wKb<)al~DcM~gjU&412V$h4YYi&%zu@%0{2c9J*ytT88=y8mBW60*De
zHY2vs-t5M)oPS`fQdmXXqH?$_xE@Qb&)1ld`-ZWwX_31r(+Id#dJCOy<#Vg}y=#&@
z-#;0%ra}4lwj|<ET11U9@Z|n$K|*pd&+usNe>V?1NBxXmjwd_kDPoA0&Lp>C`?+oV
z!pvf{NU^;I>AfJc$GmCLOhTe)w=qAO(cB_PETX%;<<VlJg`LMw(^eOxdT=A6=k1LF
z6U8LmD-6*|W0j=s;<scjNzO$bxfmOxsHrYH@sf*&jQP_bJ8nxt_OPRj`a-+x3}0$E
z<IakjaYbJOY0_6LCQbDQBm0!G*y)kws|<;6${8qphd3+iAuC6hCVB9RF@GAe7i~$z
zk+pEY(u1>0uplA1=#>W_7%op$k>xK;^KA}eSiB`a(yGbXKlziGm8E};P3=D$<B-<W
z_GH{p0=_VSCCIjFbh(PiUkJ}1vA2W-@_UR~)U54Yg2W=a{#z+8e02ESc_&nYI21lQ
zC`WyN%6AyIPZ=vEEpEF+{y68>o7s?efnRs`pLo#OuNZTv!S+kGBxLveMMi$1-T%#9
z@RfTNqu8cs&$xcgSmd<0?(BxE+-j%}>4|5d{e>}i8m50{OQPqPZaa#jM{u^VAFQ1y
zR*E=~I`72}@9*A>5nQ{YSCR8}T)yISC<f~(aF}Hdl{r`9X=$sBnbLq;VN0Utz}-Ja
z*Q$8V*;$;diSl5&U#D56eSCQUDC&g8!Yj%MaWTjAoB{Y1(qa=hXsoKVo%ltWhLAN|
z6S88>Cy!z`I#$@t!xTi_u~>xnWsS|Wq8&V8nzhfEbIne&wj|<qViA_f$c{&R3lfrx
zUPX2{9HY}hYqP>8X<GQu$Uzw<M8}PJ+OoOjjWwRu+-}UEc!78oHU(a@#r#1T7O3Ww
zu&(SH^Q9TnlY+z|25$E%n+qIw%7y=^RA=~rfpFDL#N7fuF`G&}(zks@lo;688!Ip^
zu=iz@jS5*IXBKku{_tpYE}-XGV{kyIGTJ0=3;b?lzBS-qZA(J--)Gr&7CQ99UU5d<
z-`%rUO!&D5L?|-agXeF~SpTH4z-h5|GA?r*l!J)ifeS>K;Rv1y6_Eecm^Tg2|6)rb
zj;Do7m!kr^6AKcOi(ZWi*CXA}HGZ_qN!%Mpv-`bt0Y+?&KX+3$r!O1pJFPigpCOKf
zS0GL)izKpLv;q*B$|uyE{Gl;dnj!sxAklS1Snxx8*NhY!xF8<4y17&FWJ~NBYyRJi
zWlXF2wJ69_c~QFU@}xDhe>LVr)AqmEl8_zdA8kp<Zp)Z2K)1CZA-Uih6blkk7caJ5
zw<0dE&+Vm)TW6FzR%v+C6PQ~0mloWELjc9CJY$$8<6{bhXsUI}t;~u)*n=Ir439AF
zIXLec7dus^_Tqj2X1s@6;zg9zB;JGY17WGs$aM=djV6^PmMXnFsZ?p$_*=Cu^CCf_
ztAkqjQygy^t~h6~@9<L5o1t+d4q7xGnYT52v7E6YlD(Lw{x*dpS=uK_{L<**`9_x2
z!ppuSul5RK&NPUguq7dT^2-_Jg$^#4A91IU)|rZ87JkauJZH>)T5MLJ$c(=~99#O5
z7Jy!7%z=i%Yi&uS#=v^<9L0S?3BmcpvG7ZO%&>gGSfaGBQ0<@KD2U*dIIAzIIO!*h
zS<&$MF+rlM8(Z)=>xf^QG)wp9R%a?Sc<^Nj_c%ujwDru-%bxTuc_`%Oo9H7`@@S`7
z2!agkXN<L(7T6;}6%<LGh1A*bdWLMy7h25L$|ST1wUL8~%+F;G29}WIFF$R}y=Du)
zX-h)()&FKoLdIWgkyjLd&EvJLJ;vH|IL8`+5z@<EqZ#uT@rP_wS6$zu_@=!sgPoe-
zh8FDvH;OM$Ncn!-78!Zb+&2Y@#q$+eTB&hyy1x^6YNY)+DaTj8F&f8{&@g<L!P?m~
zu(D!J2eM=X7f&ZQ(#+pKtFb$jNuB*BV@9>ke!VRT+1a<-l8~L9P3qreXTL#CYh33@
zv7Q%O`Hszc&ZCHEql3_O@wV8%7q(>E8Z%Z_+R+8o&He-oRkjmg6npl(Sr0`&U{i4l
z2{jjIjhWKy=#(JQ)hR6eJm~(1aJm7C9WUXi`l2Y7*{hb*q9ZwDPqon1@>#E5$FVI(
zF*|jRCmv^ig|ryima!7kB71+P7|5EPWLec(T!@-&sG?$>u+a>6hDESX@*;I(#x?Y-
zwj`t%DYF4B^b~6_!AzoMIbvmJIPZ$S8|}}gFS8-M(^wg44Ph|B5TbQDB2nw+UEvC%
z$R}j_f54bi4f&2OiJl|B7noxIC;UU;7jf{f5A$Ax5{+Wc39Ig+xSPGosDIK}5ouA!
z;TFCmG+z)YHXJ3-5F?I&97$`meWQt`As;tpQ-l0twj|<^Ta5QIWZ(hEf`sH^p80|8
zX2Q`ExGt?o=Kut7-NWYA;DzO}rsvfVs-v>AkwdegU{J!2<?vPiS=25Ulro#`-x`CN
z)@(;JI73G0L7J_<sZrdF-0;<a39e8>IACG3bNHtJO!Z_5r3ZgwEQDs%U$-UEb934g
z+QgAC!LA;@*{ulC;5{~l%{TOFj=Cqo6x7o)lWG$CMiW*PHyE?10lm(a#QznbW1)(V
zd(<l=0o~<22KpXj6{PL+NkGQ}6d!lBM<?8`b*C|l8qkMqN%S1(oi9Bhy7E0pIrGw)
z*2SX;r+I;)9XA#_Ewno`m#4&dhOD$;S~}@qm{T!bso!XS!I(D<)0f$j=sBkAq#MSG
zwT;;3JMAf>^*zSoq(!SAw_i^oA4tbW#EwJk8|~Avd5tkA8aCf$OCpYqMNB8dLSB!x
zAR)Q9*EU5;TwvM$WsAFPf0(B-#k_|Lxcr5Zv-M&nSF9nRq}*w>LcwTw>B+#^qr-b5
z4yN>@;X?c`8KanNlB9&hh4>F7EyVw#@waN`_w$0pA|{{?Jw(N_`BpAisybQ?uXU#E
z)6r_Z?v;5vZg84?Y^}hK1pAIZHda|mpxTEsMJbtBkIIFT`GsJiWMR;*tWmB?N-}-P
zm|YFwFW8chUHfxvBnzEn+Mr^M6R7IN*e;T!1`OlXH!e)jt@XF|MyxeRoTizpFb}*c
zX`%l+wi=d;L*KF`ks2iHWxK8Ct21srFOJl6-~*|WS3i%hYb(ZUchB(JmT5A7qRZ8C
zIHJVoPXR6AILup&InsL8&4R=t`p)amV<SYEe8W-2w0N0}$J|XBl!uH}kk%L8AhyxT
zosrCGA~H!{@3=8z8a795NyrZMVup92z23IRuwj`7zo3SL`V?(Zio_P}?#&pyim`xc
z!P}mhVGgrFy#@6u!J*ovl04x>W2Q9V3brJA4&3NS(VeQ|Qn}*OqSHCz(pC@Hg#io?
z7gX$7ooLdw82BR8rUy|a?MxI=Wh|gsP|9o`Z!=b7TJt!G0_#u@!s)UeU?G7XvaAQ%
zbH{pM-yG-xEkiv-EGofGztva{&7$6HOCoMj7Skg+!{Wihf`sIvM`74z&U)oA?IBl$
zF-9NWyR>0j`;@VQ)7sjW45z`sTei#4o=~jwE5`h2HuFoiBzlh4(Gy-3HNbT&!(k?c
zJ3s1PlV?jbd&05T7yacD3^A>;TX!CH%Po(N!=O&rz|d2zL`Rwhp~#l-HDk4<wS<w(
zR3JCD@Tzi)xfRLHwLrN0NXVVNL~>IZpdz>8Z4&A^{=%4h&2Ik8mPF6(W)t;oGLc5E
zc(K^bmT^3K%n*{BFtTfJ>eYm6bB21hsK!t^Q=m#%x>{w-jt12VTN0_EvY&DtadEQ*
zq6i8fl)bjme!I0>D@N>~v3zO!;x<%T=PuId4*rra^IDa(47JagBMr2yAhC#3u5}Mp
za<okcJKMzY7==K4%Bp|FSkSbp??;j;<cEetxp)ATC(WhK8S|nk{fsRM*;7t3ZVNq^
z@~egskmo6OiGmY<$k;q>EKFK#f|6q5Mu#S$j%dc16%CrEEs35()5kY*=&+~{7?SrG
z3y~I*RsNkDIvfd8jdvNdp~3Nkwj|<kSj-dTRD+$Q1qsPTkER;^XE0*aou-#7REkfv
z;#-CF$7~?KYpm9^2C_cGhc+l2DS=1A3j6OEv!WTvZ`qQF!()N4G!kC;u^=J2_^@r#
z+F$W|`Qonltvl_tx?Y`I`f#|G*XJxus7vA{uH}6^X)SNL@waM*wN#K;#Ng=q;~s`O
zB%x`609O)sS8sdnrrgc<7^@(;n`?@1-;kM9IK@@L#A(8f!E2Hts9nZvX~<-3NyyIT
zFt`gHL0x|om%CKUIMIzVn~vW_PV?>3(Rga^rVQa}V?onHNLROnoW?MeVep!yy!5Cs
zTN;E9+LGuwgxk+~xfYfzQLHd$7o4LVgV!(?F)esIP*>yg7z0jO0AG`|Nw;RqlZM>1
zEs35Zx781aa5Ju`K}o*Uy=yB*?H$JArA2L95F8PIw68YgFN~4aBwexoc4Lk-%)Z~2
zL~6{elQUby)t+T{7J)1d5X^Zs)U#A#D>~g8F=U@GmM$%1TQk#i@u=V~t8%9xmXP`U
z1!IOZ#6BuWEMoAniz`O5m-u>doZiK0aFOJ6bJ)Tn{*I1eX<uTTzGAGFv^ed;o_4Bt
z@wvyba?Cw%M3T&_uqDMNUpD4Y1MUxPNyx7D2aNqf$0nPGLq&AEd@i{Oqqp?t9>wwP
z%^7K5Nu^wCu!$%k-?PM+Aq}j5TPUzBCVq16$bH>{gfy9X!dYwYOv{&!^ns4zqMd<L
zC(lMJINS3#XInaK3|(4Ja3kMDndG&z)8(2P*e0xl95CifGpQj#qO0Rx@H0&A6Q^UL
z41yLors+&9F<RjeKU(c*Rj~E2-oR8u+;)Ds2tO(A@Jm{izA{pb>|@3nOpEM&nMs@j
zmW8CWR6>$wSyHiV5(>)Kx(9;NGqL=xc>cuWL(d!Yts(ysTN1JxpSC3-oeA6Dze{H_
zGUC;Xmx4lNTJ)lWCTlnkRM;eyM2QOvT-D>qs0(-A=Dl(>09hE4Y`V`GD?hF29?Rr?
z$%U<o<vvAnQ4A)oJ8=1m_;|dTWyuvAPS}<9I%D=V3x2I2(bab?{P77l3X1^zmoQ_a
z)pYV%ID`nLUiDTvR>C)5V@B~OjMb19#hVyK=2k?O;FgX|!Wrm-TNR(A=<>&mS<?`D
zzby&rW8TXkFLZQy_z3FJ#DQ7GCWfLZ56Y?Ubta2->@n8*x`&e@^wJqi8Vvkrj8&5s
z_`{hAU$!hNJp$Qq2nIyM)_1JktDQL%En*G{@$09J8P?3<H*HDu+#GH?UZezQ6nIqW
zhT4f-3pa0ThWH<h#Z3#blbIxbV`$>4$dKO%nhDFDe`m~_hUWjaCDC&<_b4|(k&8YO
zAG^ca^yu(x@E#+)eP^#GnYU#c@-8TH+*-$#GDNUWsEFKR%%z6)Mq3hbtSurv87%UE
zZ$UzG;fN`;1qsOoFSJ>ZkX$UWoyZdx*e&_^;;x+zpTU_h)5WL77|1g<I)j6jQ*p*z
zlQvM&OzxDMD-<!aJ4wZb6gLYKZPEp8k$zVCRJhtYZfsY{E|L_IxZ3*LNvo|FjK5Xu
z5HAxX7BTkh4^Kb|j`xTRN%~{<P~T-NYDxv;w)Oa7Br;o(Sc~?IF)NzZFWZukU9ZK!
zb&DshQ&0W0l<HmXma5brGd}M#mMA?wv}EdCPD)WOPfAe!h%qY~K0nOLeG*=3&uco*
zb_Pn{M#n7dEd3jPJkeS3Y|^GNRKq)b-xE#FaSr=*Y%M0a2`m5CjU`U2{LPsLILih%
zQ3afZBQ*d>nsWWBF+&;zpR^?*yU53FNyssh4dIH$$ay-d?Lkx9$C{YAQ^iW)zMMpd
zI!8M;o_{jdQd;BL0nf@jW&<9B^PW@}n-JCiy)jRk{rs&h328sQLJl6b)I#>nbAqF;
z96k>}5?!>ncW=kYU6)yys9g&>+B>j)+~-lA^y2fV^!<bweVZ{)8giR$N%S1KfpZ>i
zQI`8&Pa;ah-X!Jpm$7P@68}*k)4#^JzSvj+X?x@zRImgRh<&lxaRdp}14SFY1PaUv
zOYQd?v#7y*k1dIw!+iTAwEaqUPT#NkofJzvWlSP?r8|bS(VGmmXRL;_VBel;ksPuE
z`*H-AZ)8<56u1)>=q8Mr)ZiYsC6O9#>yY>6v1S$dWE`%U)6QUY2F6Z|#FkrAv}bJJ
zXe@JDY#j`(0na+Xjj6mkz$UC(yup|=4bblqB)ZNV7d%G3>1@4<@lu?;kC-QrKJ2DZ
zpF3}BM(C%E^^hJR#PP(hSYeX<hG<0)N(!QX+?Y2FqaU>;Av@jsY)Qx<nk}W5L9}((
zUw;bIn@Y1ajg)x<+pkWHM9nDSIUCL&8>=O);cUoE`#jP4zNk!(cIJfL9A7f#MKhEy
z2oha8^TIDdt~hsUOe{l3^({YUHGjugw6tnonW;}V#0rwy5mzL2#BUihq3QP<wj^Xn
z{3nK}Tf)b79=UdSWcZX*<;190kHQgu$e`SEORwe~>*%y=6q|(HC}GKpq+HL<#;j=2
z+-OT84vmF_lRlC=paluZMUSSdYtPoHPLK}PXclX+fH(e-4dfAHm8R{r>u@8f&rMcv
zqZmlSsiWtN8PUw+j4g?t1G9a&)vQusS<cB9Cnr7HJ5fwn-k+l#!}YYWh-u;4k#Pea
zvvD33<Py##oiXM~L#}B{A~kZ>VaHaUVG@9p0^+ky&YT>Jdf%?C7`FEq%a<0mZT`%W
zz$5<nOp<C@+2*o*bd`RP?7NIP(m?w`L1GaDk%KrIq1?*TgnzDSptx)*z$*%q6$OUy
zSBw>w7Q#bx!~)BO<ZCcl3MU^y9+r^}a8@Is#DhG4$(Uaa;9s;QA$#S|vyCitB(!xH
zm2fAkQ$>*okV(BL+Fe^QqJLp5URp%A;WE{Hw(Rq#r0k-oB~(29nK4fqYX8HQL>x7X
z2uB7<>>MmeNG>=?vLGS3;3abl5|WEYZ5Jnq3)D?qSbEh}U43NN%!~DKb<5Fm3oAHO
zjVH=QAE`82C=f;_8B^w<N+QxtO1~eLM(n+HVP*>?b7E=4my${&_85PwX7jrQ33D~u
z&crqyMWHp<;||x5JVF&p{Ah{fChU1ejU`U@Jer=dQHW~98@#Jaaux<~tCJ?p4;r(h
z0dUNggzRWXY)Q!O&Sr2$-92+9T%fx@vAEs+Hke<zh^{^2HeGpiJcb{wG%DKZzj`g)
z(sKIeb;c4W8zV^`>-4^vyb7L>zw5Qe-=$gFs|1OzU47xxU#o-j<MnFn=pz>K{l+4u
zRA+llc;36<L{sj2jlWq_?z?SC$Zq^YjL$+xeCva;3#S-2al&HU-6lL|JbuGiq_lW!
z$cQ5iY27a5Ndx8AjlW(4<yUP<^c<Ay&(@L3c&bGU?f!5`HN#W&YO9iu_?!0JO&O}c
zHWoB3R5u_OrEn^SM}<=jwXYd-qM`N|wj@%cW*sAnJ*YXHwcYR>tfe~5nX01}*Txcu
z^EYSs`gbi%^r#Ia?VH7xRpvMgj<eu6>vNo?a^OZsgK&*8HyVU}g2W;YHCYsjHN~8k
zwxeRV#cZR<QKp|}7b!!kea>UJk9IJ_cNwz%#!5=t;|8gqERY5Ljb#xXPnCrZ`4F;b
z_zfDfrr~$HEeY8L2iOD_dNk_~>eho1B#Nc}nBjWFSfI3Ut;g{YqLQAE>BX3)_o#Em
z->xBY#+F1J5sMgKP7}CaTab`k%oF8UyXMX3XgI}X*v@%6vam`yq<E;)r45_N>x?y=
z)<m|TAdPv;io**7-n5?kT4NS8J9(8Lv51~`{n^=Op;~c@6}YW(S*r_7c+UF%QDb4#
z>U%?`CV0Z>;~`I)uHR?;^_s4K#Fm8Y4L{6qEwq=!qKY@!!YRm%O}RKxck5br=-P?_
z`YmJe(gH+FFT`Kg$4T<1!Svsanb2VRlr4!kOcs7mx=!wx79=DWzU!P?hIGMlEO+3N
zO0V;_PU7Z`71v&M6>aJ0!%ytxSj|7y@{fN0u|fQ3FGC!NRn?Bjz_gdsUNJJ2CprVC
z+bbH#&*f>m{OddTtLag`4yC{J*UBdD$;8c=7wJ#0y_(lzyvbsj-!5If8`+Dc^v^Q<
z<F!{zyO;AggR9Va_&fNoy*x+Pm*b9ucK`4MCW5>}UqsiPc3<6{37(f$<Nfy1sd9Cq
z^L%?*p_tEmmCkeRHU3xnA3Wb)QE5$2V7ij;j3T4bEH=SPwNjq#JjYfc)z)6tz!&8_
z$gz~_gFC0$=zSgkBW$LZdhH8iJl5qh@PG|?35tCCFaF?F;y>U~+DXd1WgcZ7p#pAi
zhJxL(Ad>Vi=04|%3t%Y4Ek;Rku&>B=pX4g<;qsIRUlCLPZN}<9^_SNq>A$p*1lWXs
zPM0=|e?=l~k(9my^{+K#`1i9h`+psX{B<z$*CFxOM!%&Ren*-e#$QPt;C_<!0s3c{
z{y9qj9HW0u;Genr!)@Z=9l1J3=kJWQYPBl%Z$8S0sQGblBL>weP&Vr+|E{6BBckU!
zyXg59q|=FcbehGzDoD2=R5=g7i30aym4-KsO=!+3oF`Gmt$CFD71tJ15Fd3wh5uM;
zy-TRI4{PWCidPrKK0}Iqb{@rU8!4i^lr)R`wwklpDS|6Q*{?^O_;>%u(&61ghbsf0
zvM>54`<U=4JDKoL@-j&d<3EwU-$DPplm2-Z{qt`8GncQ}JmPt^aDg%~=9vAyA4GcZ
z7A&G0B|y5Bl3*4jWPtQaLBcJi4}Sb$k+{GT)RS{9K`s3H$jb9!0HtmZFa0gzGLA<6
z8(?cqm<W^{f|FqLtuXzX@poyF_AlF#kPv=Ckm$O)Z@cV#5b@1L<ZG)AZY=gLxj2BC
zlcMnY9Ctp#r`eGML*R6#QLaWaOI8#Z#lJFEOG*^mhcbv8FCwd3MX3zSg#*7RJ&lEI
z94CaMNc_X=AQxo}t0b3?n_OJ{b7KZI=)Y=9A`ZQU1MqwA_*}iWt3_ESoHm{JCqp7`
zIDra63L7FF`QO5hym=tnk*|Wo!L;mZ{;`yQEaM+5#E)bXAQ=-oa(iXn6V<Vp(y-NS
zF#bvgwY{FMVMG1#IOc^eDkEzJpW-?}aS?M%TOL9Jr;cEvT1S2fmlyB}lMyAiyES4h
z++nPRl!|X}#iDY+9WqUlJHKz-if?8f4jS{|lh_-Q*k?(iy|Ro|k#Q_CPWvEc1*NXt
z!Pdd<61{;YmctD!y<kgxv*8>^LeN2w?NOBJ9N2f@Koq*PFR>aQH`YN~D0gQ>VguY~
z-H`k2B)HE8+-C*%8niDp=1GJ05nB@H@gi%cN0=^8a%%1HQ~vH(8MB`jl@%E(%O<C)
zsr{A4U#h8n)|NzE?Q+1H->EYf?Nt*kWXhZ4)Hfr()tpvIva{M&qW6gxORrem@$=qj
z%4x6jAm-l)XK>s0q%&NvyR$qapQek`{Y3V0&uzBqv1Ogo1`L>Nj30}sU=xq1TlmK|
z{;`99TrYkk8zZSAHq4f8MElZB_{RrP8*o2j{N+BSt^@ARF)-au=vnv~Nwh3Wjb+-=
zN38BI8B3f}-SLVop?oAj)q{9`6JO43e8HFv-y8gHNaAyrB=qt1vx3rskFVK>a3ye^
zhE<d&(scwP>hv#EC<HwRVcSJ_D%L;RhVQacui6uhSQo4-wFfiBK$hV4aqu5puMo(B
z>J(oV1^lq?UxNQ_OXNxt{BPQl_y%5NZOwza^(15S5x0S@Z$9LS?yudY4KsFY&tTkw
zt7XAk0O4fdniaUhLysg}JB|6#`sz)BMAvD^f_FVWlx)(SuC^MCL@wULlAACBCyXUd
z>m>bi%a}99fjCnEkVWeff)QheGzf-mNyr}aB1;lU!4q4A44&rqRlY=$oq0O2?X;->
zbZfdcD}1#pLYn9VNPo;WGi$7zv^KLogC)}1EN^pQjud+d&$^P<a><w#%~~3^Bzg|d
zPQKMk8BzV!wKLfHBT5Ei6`g5bU<luBEOc53GyYv)NQTh0M9SD%m0T;Jh9_G|P=Cmn
zIStfz*pf&Ms`a3`>4e+DG~TT^$LRE@iU2a&@sgV`a=&IQSz6>aXQq74vYx`3f>n4d
zmBi|ojoHz#`h*~{cq8VXlaDx9ZABWfN(aeRD|<z0M-eBSQsp$}cesW_SZ@>+gZCJ!
zKR4D*TB!D9$}Q|4R%8&_bYBEYrQ<#g7D0tCX}tZaF_RjAU$G@2JKL9SNtE#-Yn#nz
z0ft!%5|Rs^6kCvxy7-W7z)xIYozPv}Ov!pbQzGj@BHgAmK$!_qNOSvb(Mj8FBF9nM
z%RdJB$3FgXK>SGV4<ukb|8WTIONa3fTc^I@Fkt-UKBcbtk6Q)Bu72zPdj8|2vBW9W
zt>r&R`IP^-Mmm^#@#T#AL&j|Q4(3Qm;<zOVJ^yi3Q0h7fvmVs9KUTm>EjD!o8vufB
zZ4X2`Lwk;PtgX7SsA)mlo|yrU*<u4DLu^YxNdg$=FeHFgW0o`k%dGarOBL=Oc8*h7
zJDtaaTX@73aFCjo*lAk|raZiHC5le!w-~FAv0~C<HIQ+$lahbF*$w>DQabS;ZoKKJ
z&?WW0?=$961MhonNnFH>tf@B%5?#BU?fi9f(Cb`hs#?wSHEFRP*QE{f^)X}7(jvGy
z>}_tAcndBvQ+k>_OilK+j~Fwg!S!>tB;s(%AZGr7nz=~IesI?}r&W^dEWFbmC@n$p
zL;Kb-<a$wJiFF-1M7r)xVf987VTx<#9C3KB6ZH`(-(mauUu^gPLIKM%EXP-|To@^d
zZ?A6TiWhOWme?@X2`)Df+(jQnmph_wiRkJdEpZPS6P3^H2Zyjbquv;rE|gF<=~ae;
zrSl<wXbVQRp?ozrbQ0yhSn@hu#XjMopxAJ*Seva(bVLpH!}aw`d?(;5gJdi0RK>xi
z4nhKMU}7q1d*gwOe|-WYt(@G${W8nPG)J{6yK0yzmL2u4EVW&dQO%#TadaCL7u;KJ
zJX4=?E5)b9B4(?eu-7!P1!I%By+^TodsC(w@+A&L7yCw)aV;^bbd34Y9OVtRB&28B
zVN2pqfITagt-!?SeL*A_3-2G<J>IO%?Do<by;$)<H6NYX8%sZ?XwNdAH&$EPj(#(h
z9LA2*=Y^n<WyP|>gtF|{FEM6Kv#ZmB#G=d#{c&vRdE87F9j5prR`(@lNQ^>Dkrpb%
z#-L>vFdumGKy>s8K1_1dueMZDo4;KQG^J}ngolXlf9Y1gGx(f};csPl|4~#j<#Sa$
zlOB}Tnl|$J1chSuKzQ|$8gVa8intq=%27s!nk@<G6Q%`;1s_8WA1^kWG=Nb0+P}#(
z*viFM7>`iY;GAydD59YSNfg&b0kovSVE>G<YSMyz2QI=CvSD%+UVcfbRN4&Zi6i8(
zudm@{D=Zr&SV=b3pCpC!A2jAz^C&-QOCoL$CY{9RLXK4I*DOd#F6J4jZ0CV{$8cB{
z+a=1&`OGX_q<p@P3yRrTY2Q8a03x(dJzCrBRkp#e8pDy+2D6z)AW0Ysp?Wuq`w@jC
zVmS?=c9Rd+P)sjeMw3I?SB%-z4DZXfBzg|{O*G^ksp4GKic>)81HmjZJKklN=5$e)
zGjD4KcKP031-XtGNhSopS)X6d<OKXGScc1N63eB=oM~7tu_e)SEVn$U0LY7RblPDQ
z!fuTixxL21rR|biG7-3F(~4kD!7IGnMdG!`m>&(VUA828j@P|n4<+?DXv5)6?Gyy$
zDSv|V!tg7SmtMX)z1OK?QPr(ezIn=_ecw?~uZR*O{}N+0rA7W;9E^g!qTpOCuCkos
zU?Y~3e)Z+dDq?1Zn2LFX*M&&)IBm?dW*(!qBzkThchgWCSjQ>Ptu&l5go;q6bF5y+
zW*Hn!Hrc9(O}Jz$N3jxL_AX-&SkySKfDW6+C1Vw(HH~|)B}2$4EMuBv%r;oYtVc3p
zr$#tqAUmOzdS6DxGQ#T;q-8XWdDbkWW=kTqWmqqhtvq(wt7D&gyh2g=oT2+6V;Ryy
z*O&1^9uz3T`wt`(?=a>;gW~Oi#G<4EIAyahQ1CsDa&i479Ir#=q5eJvuX3q~RCGcH
z$NY7M>l4PRN(<M%5H4(X;^|qdmh}Y+PUwjUq=K3>KmP?|UNu-hYD+@)u>XfGW1){$
zx?AEu?Or4&JWDg8>P#hA_sl!q<yyHa4ib;#yCc)0E1<&`@l|6LrL~B=rA4raSrRb|
z5rd`Cd^Qv@8xb*L8VM`iUomD{GmS6XlIXc<+$c@sH0@O_&$SXTUt<P&dA3*cr5h#4
z70GNkCsva#eWMA>GfRy*(~w+ZOCpY>MaD!XU3dhsAR)Qnq>BX!$px1>S&)!i?6h4l
zBQ8+YHnF%>ZTB7XmpCXw#kH3c?nF_XAA`I@wazv3;rUfK)pbNYr#%wKmR>}qX<~u%
zl(9)AJ4Mn(VgcN-umG+k<Cuqyzg6oCCj^PEaZJ}Vvvr`jeFWEPVF$a|n}CZLeD!OG
z1kTZpRbDn0KBdZWK{qD2#G^>cMR^Pdnk7jQNYR)l4UoyX0a^Ik!~2z+nJD^laWoa9
zLpcD_1CA`I<Zyl-&aR@5^2J83?%{&<9B$Bzbcps9(qdr0*I0>ZfxTb7*f*4wQ?JPW
zkgS-`v8-bL7l@h_qN>;@q5mdh#x?Yxvn3(j@$1-t7CQdi09T1^9!+#q;W$T*$JU6G
z8Ze+gXDn1&KsVx)_<$>#jMws$*oPLBcc6<VE!%v^m>mtK57?6EJxs?g*HF2`$Btie
z%dPl?d_zJ5hUtsOLZycZVSB(8rg(y@2uulq-sg?k(O~*LTM|8oX(xJ97<i1JkhNZ%
zXwi-p=eVCgi}kKFFEEVXHWoT9j2Y@}0oS;4QgR(;#A3ZKp$y}j#_VaZ{<AHKp2NEN
zl;`oWUKlgQ0E5Bg=I9vNr456(b8uniU0NK8VytQKCPp>MTGUr2tTf(a%#ViF^|mB>
zj@AR%B#V33=<;3iPjxy@5~l}YW21i}aDd?6@UjisJtqLh;ULpdT(6iKTfhlpb*1gT
z56~7|mY32ffxKb(Bh4bbsY8fMHY3E1c<h9w%n@VOHDejJCDC(ZS$ht4QJqHp3!U^F
z^Um>y3}x9^n6yx?%hUxos90fcBG4qPS{992(V&^MCDC(ew$atGxoRCp5pvuUtU*OP
zX!m9e*Y_C<m=><>;-+SW!*INeI^cx$#P2m`N(1gqwj|=fS!9W1qKW6V79=DWIookx
za>412E8=40O1MC}>xRWmcMSwp{!VbDK3|B9YFa*+6vb{B3fyv}vy@JSsjV->%;${Q
z5?Fc({}|&RkMNJj#E;}|L2}1ZTfV7I<1OxjpELe0pF|-f@mWD)5ffVL9`n45<ymJe
zxUnFz+@d{YLI25E(3DDT_h)7VKUg2}Z&1**{P)IRu4(yi=hpJVXQ?)kmYsu#oZ;3K
z4i9$@9LOHHBVw}c<R+}|twYhcdclks_qUS1vj-1lUBOwFIn$KB$(RF8>FaGtOoY8-
ztsv1gt7}`Pc>|+!I7H{}>=+V;IOd+t-;BaEPg}<8USm<y_KzEVybgzW-Hr8UpV#bx
z;LJzqlkPHRMFZ}zEs353x0!*v12qK!T!*uFM*-{7hJhM47A-AMTYRAIP~ainw4Qar
zm<0`>m)Vl&IY3((pgR?S_8-XZkGa0Ctr(>5HWn`}NZWjn?o>cx{xq0gZOnuQ)3dfD
z77M1kRG9A09yl27Nj^+^D~9REjKwpB=`Ix}!Jh`x`;D2<V0y1DiN%8HZUv?TcVrLl
zON2>p#W4N0v3RC1MMfmSp9a%!7&D>4^y{`HdJfY@;q(sebI#xhb_yX5X77wbl-Ppd
z`TvZ?N(;{>c6NvMWpM`@@s%aMG-&?Xm;nu%ui295IW!y$9NHg3vwvT9|Dh;2UD_}_
z8~61p688g!L;FK`m^Tfeeq$CifY#WOSS)}JNPzCl9@w7<P`nKTbf>XsY5PwA&;bdM
z;7tSQkTDAyK>KY;EEYfqLx2t(${x5Y5ukV*2Iz5P(M$n47y`t+X#l;{m<0`>M{G&-
z93Y;c9y$~PbZ~$6G`4|=$!yHk&C`|vdzG=MX#w*mvxg3afPvSF&ujLOJ~qA5m=z7U
zSz8i42hJaN4m%-QcV_n|yS{iE2I_srqNN2YaD9g*I)XQ?XZ?sV3mQN_Y)hi&0Bzy8
z?hXmi{zKXQG2hp%(f{e~N`R!QuBgnu_H2MCMv)Lj&_Q-Jp@4v*8D$Ygf{<@7f2RA}
zo4vq{3DG19J^IVhq@#kU!7`Pi)M(Vo=90K$j1(q{TgqT1RJ1IKhAJ($s+@E0`}4a8
zGy#jDiucZa{oeiO-naIf{_klOJ#)~;{l8Uoq*>j~@~cSM?N>)n9yEvj>gcgURWV$3
zG~2JEX;V*`HnV(wGIg|HCat4)4%)bbt)tm~9Vxs0>gcV5=CEHK?KxBxL+i-@bJPFc
zb~Y^54DQ(KPWvIK@L9ZjsQdjs3;nCgmtwKw%dp6Y@@@<M$ZO#b-j;V$^m^P(yaaz$
z>N?h0%Bam*#wg&N&#2y6&Zxz?fKjV+A)^{+1)~P%B1Rp~O8xBHj9Q#k8ePn&-C513
zLT;x<0bXSYi5H=DRmq(-cmmHWg%<wW23hzQuCCU+8z9lF9rpe>51wx6#mkVR<GV&&
zU3AvtZ;AMk;)o4J=XL|TMr<rPPkKTHgttAR5<&o8WQndS2-7{G8p29XsDZHF6KWyc
z;R$sci+R_(@>y9_fW<pFmX<f1fJGY)ngy%?oTac5aHWC5?HU!TkbbYiYQPs2)&PEB
zAamzk|MFvfU?m>720#4gk)p`_BZYk6&kdBP{K*sOfp#2Lfc&H~!$8Sot3WEZ8Yt<H
zdjbpZQ-LJM;1wR|A;~!kYXL(F>i{<@tOvYXVFTa}14Z{fPoVBGteVO$yr@!Y>a?Po
zS_M-tYC{g3CIdC!Q%F;{Dx^VAE2ItYC?wO?b}2@tGZm8QDurZvg+emDQz4l?tFRGp
zufit4jt)`Q40xKtz=o2VnQrJ-gYFIdyjtk~S|RDau8?#;Gf;fdIZ|?y`*MZk-m8$@
zZ&gU{k1Hhie;Oz{>PLx=x-R;&(-YX#mwN)6daoz2so&}eXzG3on_=vzfgKg2p|wWr
zI8h<(I9DO<xJ)6b->4AO^X>t!+}7Hxh1!AfP+Kc%A5=)Sy9^Y2YQHL3_>7Yk@)^q%
z(miVx(ml5*q<bDQP;~57fxc>;AnNKQeWoYSSKTVG1zf9;E#N+dYyp2UP>O!&2`K8j
zY^HHp9c=qI-GJwyWZRJnrQ|G6;6r;<pw+HI8u}}RG<26j8v2Ps8ai%_D1@Q@vuBxS
z*W=lJ{JdVCeVal);R%I&!X5*KQNvM^yHc9W*Hz%N*BB_7e&`9L{eTK&^^$>->0?h|
zrsIwlwbks*&O%RM<IAf+OKvhyia+cLEdH(vtjw0Nq6rP9)Y-F*!3{7t#~ck}@Fs;c
z_)&#4c(;M_oVH^mH*K75prl7spjjIXl=QnifxLEl0?iun712aXPEbg`T?*OW(hAwC
zeyR|y%CEH3j17&jVJ|;#6dO8@6-XOSQ%D<D87Q2t@C0(Y(-X+)B^7Ah#|mlCamR^D
zGF_;UOuG#fmDhR#Ro>?bRQaX~FevYq-Zrj1xe4S~e+}xJNZvQ@bYomIK0jj|K5rJ|
zEQK`gDupy|n?kbQp^&WKGf?=AI$m^COFKPF1=<reP%>>&fyO;#prr5i1R7V(5vCa<
z4~?7T322&mckpiSQw{q7*l)%1u`3|#pD<9AzU>KAI<{6a(XKfLN~Vw}Fw;g);FIt6
z1a^uSJb@$f2cE#i$Jjbm+{MMmJWs&l!?8V~9>R?((3uY@q%(Ibq%*7QMGsvzNg=(o
zL?Ih%P9YoXCWRaVA63X9@E-~}3Dz};GERc0D8wY_oUgD2aGk<dz%2^f03TD>&UP~B
z>Z!)nEpYYGjnLa7uAZxq4vi?JZT$*q+b<Q;ww(rw$44}Y4(XK&Y3SJoN|rtqX!R`y
zO8Or?ftB^X3iL-uv#7+Fiu8pl&^a-Ma88Lm-h!nS_B_rEtzyr=6w;oifTYr%P6I{r
za!=snuTp`QZc|80cPONB`xMf+Q7xiShDhfOPoNgd6WHReS3x@Rp!>ZAOB?9E%?xcq
zH_$4Obf+sM-IWSSs-LhOh3*m{g?1`rq5l|2!}4Yo-yQ4mSMhEu7WO%ue`Cdx;dnlR
zS8LW|)~S*s@|}tk@hi3}1~m-EGnl|&4uiQ2<}+Bp;7kUK7+k_2#NfLO)-$+~!OaXd
zGuXo5HUxIoZS9WX^*k$=vNCokAB#uW6y@0ih{@%8A>m78<4dIFOXTNEB<f3K>`SEX
zOSHk4XpAq>Dqo^`zC=5Hi3a-;E%zn5z?bL?U!qfdiQe%gx@l0#yUjiER0!UZYReV}
zif)_MryVIxHJ)=D^u^BzUiqb8-4S7l8Z)U>&dT;BLa8_=PB_`ES+3IHna_L;crgxr
zj)m~c%AWZlY{C|O`I~k;wGMK13?~id02*<}OC0xH^PL}vwK#G)8BCyv9Gtn>ZHZ-Z
z7E?){ez)GjVN3By#!eR36y0Vkm`o)X_$m32Tbl|wc+E<h<pi+oIzl^n5?3?YIWeUc
zM>$i3;CNZ;!ZCeej5$my20N3;tZuU>q1oXTyY(17D#Pit6u4v>n&(M)oD&2pcZg#n
zX|dDI(5)Bamt>ZtVQ~Pz4ok=J+ta1+LL!TlCNE`vrU$YUm7*~TN1<95I}UKab16dH
zVV;l(Kd24B^Ci|)$C44dx73u{p&JG$%?$%Y+%Q1I4Fg2nFhIl&14P^~K*S9LMBFey
z#0>*P+%Q1I4Fg2nFhIl&14KF?$c6zVZWtiq&8~>JVStDm28g&}u!dtzg+rfCbW*Sh
z-mb+V<#}{-qzzV*9<W-HsW}?VTDS;7nvfRfW=KIyD~M$UF{~hV6~wH9SXB_C3Sv`1
zOe%;)1u>`~_7udNf>_fp#`KFV{bEW%EGdW~1+k+)KS;GA-B)6bbB7o;_-SPZmk;}P
z`03f43}$X4-ZJY+rP(vY1I8&e@nFb~i=l2^kB#FWS<TLGpglU?ZO*1~5?OC&JcZYJ
zY;<!AXGA8mXkbu-m+gA{a5U~j=eNl1wpj7t<$V^8UX%<rKEYAxQXamCY8-49FS^y|
z$P;qK9~7O3QO<dU!J`cRfM8>BLou|m=+>dKlna>m@zimZaQ6A{k!|9TvToCPQn}+k
zU;8s9+H0~nY&9G$I?p5V^Her2mrUc9;ttm0f%xty(O5=uC}p?9oFr66lIzR{4mW-k
zh~JI#F?T+N2RKg)MM&go2t#_2(o5zuua&sf_|-YOJx7wN9=s6BCgE0xaTNxkTY~{H
z5!3;O&3Zt)EFyMUMC`JN*kuv1%OYZzMZ_+Ph+P&DyDTDhSw!rzh}dNjvCAT2??lAj
zi7BFVuWkT;L*qEPXzuSe@!w|j)|yyCF1vLbO{2q`n`kesXJKmQAbw#ocG<#MIH!j(
z4-_#lN8&PX7oCsb2e)}4&a{cAf)SR;YBskWC-?N=(zv)K>aBbxMn}|hRJ6>D<ThgX
zweX61&hBL=avKwU*&GwFPyqvx(m`Bymg(f%>_s<lUIYUy<OQ0?8=l=d%p(yygJW)^
zBi$C9VwS+UQNhfm7HYY#*zdN%6r2QL;XybXSNdXfiQ67Y;FM_#H`J%rp%+PqEVyl@
zsaag0YZnXUStBqF^HRa>(5#VEp2}lM9_o#!1@dW_Xj|0o1bdOO#Xt+wWR}N@Io%`R
zx&U4~6<*oCWDXgfCn}NJn9sy5{@^`<jL{WtOVpMd+%ZRnqba<twb*U*lCtr9kHt5H
zkXRqhCE`}X#&qxenTk5PG1Dh;=Yft6a)}(sG-vZ6oC=qdS9C$;1?CMP(Td20dT7f?
zeUYiH>r$CW*23#`7Onuy=j>wFc1+EqlOecLR*ew=J8Y$4s;o%H;3IJ;)+F*tHs9SH
z>xCs)$V6|BZob~_Ko-_Isx)h@i$!{DI1_awO6{=F^4f^*iA!%v<nT&kanbx&9y{Ug
zw&{*ro5|zW945`1KmL-PyY6|zt%32;RHW!u1@RIUnp-v>4&#JJI|BXna^js8PoZsH
zeCW4FSh7%^LSIkG*t|~Kzfs~;jMzQsW1NpUDNLM>zDB|iy|wFf=Ojw=mgL4xeRaxn
z&Ll|AWCkZA=yy&*c(`~?HM}_5l=0fW^}1&buX7gV+|G4P!^&%#;l<&vjMvVqC(Sgx
z=B^m|@s6HXhLzWJ!;9l_8Lt<PKhrY2j@TZ5{~PuZ2j;cvu(U7FU7R1vXuW*@CtD1y
z(;s}Ge*X4x!%55Y7U!!nTCYw^4;Wgb=d4eEc+-``No$6wM9!b(wBEd7l%Z9%_OID{
zKN;3aoM~urJ};y7+H+6E46TNDSDd*gJ*>258CqOll+k*9<d*qLYwq5koxR|SwZlql
zwxPxKPC2cgoO8dS^{LZy?7oJfZL5%%)lqq&PXiN8p%0#aZCY~k0Sa*~RaWS41JD1$
z)XNc9y>afcwxNa0*liz{KF_ZXiP+_`@;AQVGOvu`8z*0PpJ909%e7Bk^V#kp{S3a~
zq7a4LYJVF2){M%(zg{wp&i&}hsqf7h`JFGCMxlx8$|0Jzt{!>kg(aHi)m_lH=l03l
zzDQH~gnW1gOmE)fH&lA;Jj}72kGHPACqCI(Olj_C-!D$s^W&SHC6Jt@43;4%U}b#N
zehU)pS#ZCBtBH1esEbZ3jZ;fAJQjGOI}tbd#c>7$zo$)En~Zci`E(!FQ=PiZku#Sk
zJ6Y_KxT8z6I>EI;oI@2$=VXoCo0Ihgb}D4`JSDrfXL4@>tMYo4)3~3@ZBXo_Z3xX=
zLr>w7H-gnTJ`|Zd61|>){Ryn0@qm%|Ho}D$mcFt=%W(-RyGb!D#WVUfz#9DWGO9F{
zSNA4!I9ZPC^|7U&@Vi0;Yv7X4+ta}$zT{frEdCFA?wC+2b*Y787!o!-n8l%(VeA3=
YpSN+5p5a#qTzkw#bh;SbmJfCQ7q@(5c>n+a

diff --git a/doc/LectureNotes/_build/.doctrees/schedule.doctree b/doc/LectureNotes/_build/.doctrees/schedule.doctree
index 872eea93a3f37d12967a4de39be840dec978c628..a21a5685e0d1a71fdd5ac594440dcba8685beaa3 100644
GIT binary patch
delta 6753
zcmZ8leO#B-74IJ2?(@j2Y#EfisG*1gKTuw5O)Lzp_@$L*X(}d@ehR*TQY6#dBrOv3
zpe_wx!c3czge@zXTCEq;fTr`4ZKADaZWiX2xV3WUo_jCi{(0cs^F7~l?)g6FKF{-e
z@{-?{%YFe%1J?fZzfA!T_rti@Xk6US#oXB5y-EtFJ$C!uX^)iUr(!{De_me|o1p$e
zTpb?j7l{W3_QF&D2mL-a`kK(VxW3^%sOpMt^krgOd?GH2`)N#1$HJmPMJJ0A?}{lJ
zT$DtpWWcRi2^b#akC*zrD<)Eq2haDHa3A2I?U^`qz+71j*Ih@@*~;g}o4_K#@zH}F
zYZl_6lmVD=bDsRQMlWM@KZ9PP(bm-HJ_#e`MvZ=s^VTFtSf`QwO`t|zNH`$&;GTh9
z;tc{OV$Do1#wJSG(_Otgzy$2p@zyNF+QbaCnXOran=dAg5O5Jtj7V@3489Z4Ga?-W
zhos}XN#!C4zx8;qc!&glKea)3puq`0jEL{w{PB=_Az~R+pDf`f4H|R}2!Bk@m)U;Y
zv`htc6ODhEhFjBcewi{z2rqMv^hmf%g9cyAxy3V97HQ635HuSe(XgbeFwbXIqEgW~
zL(O6Z#;0F~HGbGM@RHa_E^eKhY~19C&F$k<i)3}fkd>30+U=}ie#c^~F)rhKae%AL
z&y=uVr%y3d)!ZIz%p5I0)m5Hg=#VT4#~6y!>0g4aP8`}zT7g@#(t+AQi`|_jMdIf~
zVr=$lkp84qG6eXOMkk=JKeorGy2DJ$Xn!RTmfRwtk3WmE@s_ayhHKPtgGytRYfX*B
z@>_>H_xQ6y`5K>L@ONswH4XC}Kc6IECL^(Jq=W*E$}~YeSbwQm7i;Y190`wWY}QpQ
z2?eOrjxejW{yc7f8r2Fr{FPkLqyU4|t?t}nKszudH%h*u`?~{2=4Qi-n%p8@;6`A3
zZh`YBUC1$==VykZo$<JNv;>1aGJ2HrwZ{HKV{bLsuQazc39n?bsi<O_Dc5d*T0maB
zDNic7lz?sZfjBa+P27axV^TyE=8V}ZJlKAZ$IUSbk^^|eteF&mp3es0im`taV-*g!
z+$rIX04$iEgcq8Fo6p>tEW}i1xXmz3R^ALHr(77oa+Y#r=iL(KY4+O<v^W68*`cmA
zDU!;^RrwMoX^-WkjA~<}Oq{7|p!a+60_V;(j?GtdzoB!#PX27ztMiUFuqI~5;&Bov
zLNQwcX08$^ne`3>KM}w;!nW~ep)-Kz_A>eP+~ye!Ibw5faLkQBmYQ4ZJg?JR^IU)$
z4IDFJ6kfg`;l@BUL)bDw!VQ7k{V|4A1Dj(e<_JjFsM-f4q-xaIYf$*@1E=9G2Dx_{
z&lm<e);xzf4`u-EM(x;cj&`hWAuD)P0Np6j_``#Lq;sqNp%hVpVsaC#(ZpUuT%(Dt
zS=}5zWvPId8RXn!Kri9+=?k1gfqV}f(7tiTw;yY#ro$V7tiN4+DNt3`9bb*{Q*Orl
zr?tc9I(WPZ?qK47YvL|l*}ca1FRlzn7D)J3vs+JxX164oz4q)35slv#dvMMSI<taE
z==<=)8QUNwh=tA~pAveaTIeA#StPJ?zsWiTM-`?+Vh|sB&lT1P`EU?#d$QtoCK%Wx
zf+e}u(r|oxR;4V{=mm_PXwY-<$)ax3)<qd0R<i#A<F8^iYZ03o_Fc1i8`MWUP5)L0
zs#V7dRe^DHzJa%b_;LH7;n^3&kK5ssD@7Y6$QDPL_#xvyOl~>?x=PaUcO?tOSE>el
zc&>ymgJ{s_%PYF}AGo$DCg?wk!m(BtSqT`oUgF|;9H+|LL;o7?Q}ZfC9L_KG;Is23
z^p(t;DY=8g)w(;?@TN%~5NoDLbpzq91suhjrhl2WN=%~2Y?vTf(<`Ohpjfh|i)dXB
z?{vdTjvm^QMcQJ?A_+d7&f;cC7MQhoPSxcU7>0G2^LRRtU(J$*Q)(*7on|mpmQpCB
zLISm1O_`_SefWsBFoK%%goF<y#y^vbB~LsmPV=sRVIu#+W#FxgC7hH>jpo^l13_G7
z&@2P`j@#yhIo!4ohab#E#_2j(up|wF93D8kN+GI1%V4+%EDaZss^cFu@k1$o`g|u(
zlaJO;pK;#i@KfKrOu|T=z<MZ5C9o`<pbCm~_-qqi=#WD9<EOuBfJ%p-vsDVf&sjXb
zT&mR`@{pxQZ;pwsz;RXSQ0}lYJF21t?9}wd2C$v0bF5j8sVm-h-q9O5#86CKDdG1F
zb4yGV5h^Kd7OGL*U9AM9O(>7mMu|pG*8Wc)5f|~o(<!18<DO}RAXoK&O*Or=x_ryd
zH^jna0cTVX5)jKE-2W`SwYvP&{O_}EFwCVUL^YpXwOqih3NZ-R)vgl5(6fFZe-(^d
zEy1H%OATwXtAur|c@triu#<2)9rj&w5{fvRTV_HEZN@QJvvv$tugZZ+&WvYQ4O2_a
zZFP=-DqBG&t>s17kClanrLw!KmUR*;bXC?_uB)<UAz|s&QZ=9on)Kddej}mL<r`q}
zZyMk|Rly}_)k)PiMa>4OR(sq4WT}U1xvAhC&WSM_B^-45F1TysgTncj%X4s1r}@kC
z5;_^^JZ9n!QZ)&fQy(rGaeRHc5Ny*S<yxsa9rM!ECi>&nO(}FnwQp(^SvY^2$60J>
zhU4nrCb`y1>&5CuZh1|JaSG>F8eD!aKGkOR;<5^r4;x>QVup%$erZtC$l{aIwJQ{-
z?!5d437Q<AX!uU7p!jT9t~obr&U&tEsbSi{yw+OZ&1+XER*mKo1843kagaHlG>a{G
zaQkIA-iwdTQ`od)i8zj)ol*8RhdgQ&ml^mcoYGpY%?EeVgU~OSH!Lt>kH`6yLH<a{
zbZG6xlVk&0xFW|UPs7w*84%aYXb^<Uc6|tG!Tji2Zg^6H>BQVE@)S8vX_Sx~%tuiz
z{W~gHNrAs_+$5&4f7L4zrUbJJi*!+Cw4M#~ImTUKI7=u6JyUk?VYKSeQ^unio5Pw$
z2(eyKx+{&RPSZAO+FhJ3rCGvGP5rciZD(rNniQp^{g{AET_svMvUab8Bf&fq-|TIG
zFM_%MUz1NiwyF&sIeLr*S?aMh<#h>XOc&_iQ^9H^u;ukGr*{Y|5gwvWCj5Gz6d}0!
zy(ITpLua7gH`Y6e8ahxzW8Rcv07F+@g{m{q7DC0UjkLVU8&RGr15^U>cSjK3@YWfa
z6vFb=81IA-B_9-X8mHE*`cx|Xd5eTuAv{fUT6PIo&LDTS0WAyR2h#Jp&<(oK9}h?p
zhN7J{23ALwo+rCPfvVQ}gO(mYI`}fQXm&JVZ))~qn*9@9U!8Hba(y*#OL3%|-TEj{
z-7%JjH&6xN>GJl!BjI8Q>l*!T1Gu4l)<uT$5#RBLiE5>Xpk%3YZk<UR6w0^u{`csS
z63U{Ud+(o4Rw#>_p-58Q^#+^H*lv^XBnmG5fb**Y*BT%dsN}M(SAy_&ejtW^coJrX
z^1Dg+VF@!s`6V&`@P1gVb8RpbkL$YTek5TLfoSByzH~72NEV*@2%%1cHyZHjP?qht
zy6T<mbe=cP?Ku5tIy6vH`Y5!E6rl>X1SZAWk3Bfy<5D=H>FW){hnoJcn*Izsv9XoL
zKsRKQ!F{H4Sd+rkJ(1sLwonEBVLT*Z?eqi<W5tTw8|Z^M;*&1ur+oVDWa%FzM1}Pf
zjVyH}ZZV0j$3w@GFzlFw$S~IIXUA@L^1@iN(VWBCYM{3fG+jjE{L_)RbmfiR6vC`T
zVop$iv)P~*;q>DfN)<a^uO2@Fi^6!k8Vsg1jK=Fe>^SofJfrthb3)RigGzF@8O(BB
z)`b&mp+VR4qVa4|ILBJcG4->XG3wKm@D{_|?FP0lj1~Pv@2O35ykwk5arMb`ahRBR
z2pzk=@0vYXDwX3Y_^0OIVJI%>_UHY%0c1EIgb`TKp%22H21AY>b3>Mz8|w@R=l3sb
zLb%bP4-V}ZA~V8Si($l>4IT}B+0YVHjm-}oQe3#EU|ZYrZn11TOKIgC6o#`7yA02a
zaH|7-O&4@{+}*}e&iQcj=Mu`od326`zE-SZOrycngj0X@&nY`~tuJB37um3lW1UwF
zZ0nk{$+-Iq34~YkV#5t@*q6sh29<k{33&f1?-|Y8!Tu)W|Fh<|=BI1UW&p(~ZcD-3
zRR9ru_PlBWuY~iwbbb|pPkprs`b6+47pFixG;Xs2l802Yzn%5L1AosJKfx!zN$N&K
zM6g!l|8a{mI)Zh&opU*R4Mh&atwnIpiUD}|{8*fK{zZ65V_!4a2N-KDL%rkc^-h_l
zUZCN>GjRI){w5s=rp`xOGVP5T;MlE}VO(bhD0$~ZHqY*S%ugI|{^H{2eh|@{Zh-6Y
z)TLMm>y1|~^@Z@>7}J#i@!Bz^D_%L4bPbCR?S0*K)e+@$7tDNQ?!uC?p~a7uEG#Q5
zt@Fj<(XPQuUZLWAFRROo>hglRoTne?edp8#=tn}|75eefcRl^w<%?98a{67wSEeqn
z(U-aJRdx9TeM|e^R+sbi;p01}E`8~((HE^QL+G`|m#8lH(gV+TkGd4l5$c;7x`eLt
zXtaIB>N1UP6yFqesaJJupv%H)r=*~`u<Y@9RC8tBAfGDO%f))RP%jth<pRB2oR<sp
za#4h#TD)A0mkaT75ne99>sz9ddpWU}lX@2x#i%r@B}KuyA2$jrhWoKy{m1?2RPEt@
z1gIWyKcZB7xE~d&J=~9dsy*C~4^(@&ADyZ_+>f|4)gNw1it5i))s+dVKirR*%H)2O
Qs`jXU_+~V3|Mz6^KW-M2O8@`>

delta 6945
zcmY*edstP+wRZ-A*&E~$zosftK>-yaf*ADph+s6<*HHq+Ykb5<wQzhAAm00_L2FHY
zpaB*}^HA_gq7o7CnAAjz@zXqPG-A@UsY$FPy;^Uh#T$#ZUmka@H8ThNXE<xtZ+^cu
zYt77_y{Wkuw4psHczy8B_J218&+RArLtNjUu(qG;RON@xt;&C}EYQ=vYE#vQs&!QZ
z^Mie<vP+lz7+(ss#HGPc;?If+^vjLSPs&fm{&5nrh7E_Q2`kkcjeL^*amGJWLUykt
z$C|m2mbg?EX=EWI`x)d4jkG2q;~`_fShZE7H**9>1}M2nBm0|y8W}n8l&qGJ_DQ=u
zDB;qfXW-E!MLpTO6Co<Js~d0Nukh%AnM;TS$V^u7B@V#(WbaP$M<<UIvQ@(E*@<qV
z8F)oP+?;f1e7H`ub1Y?pmHd|3;GMxrv{A(Vn}hcV5dmo#gCKUDf|Em(3=5(hK-&<5
zf!?Xp)W9IlYobNEgN#gnMmyHjtX<Ov3NglFVct+BM`~P>8OAs`HFT1ishNLn(QdMV
zJfTNelScG}R0xOIaSO#V3wH(^cmZS%yC*jUQBK8HbeD<^(3H^^+e;Z9`7E=!DF(eJ
z2%_WqL;LV?@apiL;)tb#Wh0b)ov~1ZKMn4}hzaTg9q4-&>ZTg5Ga6p%VYn$zK&b$|
zyOUfSW?P_u?f^3$D^=0j9UdKdRo)Ha8m1f1ogl8^ol$9UY*dj9?+TaVQr%(3>vZKp
zZ6B>f2xN_o_rE=Ql92Hl^oRk)G03qd17SwiBhI5;xuT;rJj1{}8g9)Hf7aNwLgp|C
z{*kHV3=J4=@Ev^1HQ!PVZO&3MUqeT9f(8mGf2DOt8fR%&-kAR!cTw)@%G;s_0f(M(
zM;QS2X4myldvy_Z!Mq6`S)~Omq=J$FZ4(wa@9J32XpPZ^;vI%V^W#cr=-(gDa=y^e
z%NjbyK>x&0*P0b*nqAj2QArGzN@`;d33vmg!NO5VQ%{D#yq~qmuwX9hSi`P@xvVg0
zy@-YEC(_(Z<LDjC$B#98LEQ8NI57E|7(p;NHATrsf-zAsmCr}fRyaB}MTlwan?Fs-
z$;KB$1y;WZ<^nHdcZ65Tr<mP+%!th)laCU|c1;E`yL;vI9#D_)i5>oNX2>JjA1iwK
z<EF<7Od~}<-gtN1Cx1}Ozs8UW2C`p=V9lC<1e9efvBS#0gNB|w?e%j=;gRnV`*1k(
z#C3Tkm^b-#>>mVegS_rULyrzzB#v1mXgxI{HQ@Z5wH=H743pav%45Rdmw8$6(>xF^
z+@6&NU*stn8p1m(YNiqan19U7Od<L)rr|%7?8_Kul93MJAZpfCF%n@OIb1^~2OyYT
zi2Yd%Y=2UT_snFS$Bnxeb&%V!^;g()3y-?C*+WDD#Lj8LvkXIYy=KI-n%|mh{F!st
z3-KbHp68J-;LfCV5(76IN2$gTUJFNf_zW}rbsp|o^B|eL9s*rRf(zZ5ZU(G@%()3r
zH2;#g#6;Pq|D*e~{-(9AG2{sY`I@z0-a;j>cIa3y5|EDN(>hps>4fr$mt$B$LUB^L
zaAVPa*(a1wi2(?}hv#>T74>u~K3Z6OC)bROf%!|)Wsgug_5H_}JTIK7p`4P*tN`ot
zals=&uO#>9W{3tuYyLWCk;X1ytdnN|^Pr|6z<FzRhLr!Q$>uLpvP6^3>>#rb1{)Gi
z1}6<aB6tnWU#H#{vmk^|keub0WnCzr9<vP9flxd>8pSDyTIrD|wak;o_ZB8-Zah4=
zYAAfVvPAw#$Fp{ol7FDo+4bPu(i8ed`%?>*yrGHa7@})r>WFL?GElr15o~0>FI*=g
zp`tVm4nM7Axay>!5*4R6p560{l!$}0b;-^=!`}z3M7qYfwg-oEI*J1HwD%{{c=stY
zYCK8gLf?}8a*pCNU@`jj88F{q(9vlbpoPl-wy#!V76)KqdC<C%x4)k%CpMFgv%qj{
zBB*PvJc>-`!j8s3FxWs`m=F7WczBJHSRp$7C$0HNzOATsFm|nyzr~-`<6-;S0&$Vm
zmKe43sFseWT<gXJ|JHROQd~#3NB;G`eEy*G`5lkUHzWVWQ?gb&S~{n;2PrU2=a&Yq
zB_7)lB}89n-JIqY7{^0S=h9_3T)JTl@fk>(!$;!sjY_7_Pn{3zMFJACd?fT6F_Q_F
z8iHKX$bq}Ru9eFiu3ZWGb?ufJ3_3d14lO#rt+Rk52$)M8-plX*rl%0Qk;x->YQS<s
zrU6j2WxqPAXVgdm!St<4)@$gB4ydiLRl{&;Io?9>xsLkVl3^{W!sE7`m#q%(&Q*r$
z3YyjRc;zOkzT5H2t)&W$zYWtcH{QQt`#>QhTsqyHr;RHNva3qrWcfs>dp=9{cQHvl
z{UPO>gnSr*hmzrV#Ws8oYTA_yD=U?Zb@{j|GNXHw$yIS|R~TXk!1@QeLIfzOm0>ho
zmNT_*u^F1{&#np;av3W^Nck{mshR?t%7^3MiLmO0&9cM>K(2{UvnvW8OHI4dWdR2P
zQM;8~(h)$3CAI;$R>}lHXL}6?0b^cNVh_&zp~`nX@bZiM<r@@6yKE-OXW+=oih4RN
z3$$oitTq})bp&HxQL@41CE<HzmT<myc@ej2k?-~>c|`+L_bSnVaq&)ZEX$riq|X>$
zjMT0PTRW_+zSV_7``&Zn0eGi+2p)iK)koxjFe)Xq)F>H`b;QTWTEi0;#z*q3|1R@4
z?;9lKSVp?*jC(Y?JI>|2FpfG`vt8J)WVU8|*1)}FCO~R!lo$<JwdvB(zf}8Au^P%>
z^~mD;2zO}0GA+8^ux!z!)_hKr(o5yl*OVwm;$*k<{`je44Vq<x@f;?l9H_yIgXiF0
zc{Yr9^#y1-R4UHGjJlrowtf~0>pb$b7X6hO-5kcn3VMC1aQ;t6*~a|NMuWHsjfc}k
zGe)CpWk(*S_#7KWob|)HdWF@<s_hO#PAb?}e@@1Q^WNNK2>YN}T`x1j`BL$m=}ix(
zdO<^jlBwZbt1lb&N-u+;zER03*c&L3S{%+LS_G+09yy=Kx|<EvTrznN!ucj8ra;q)
z<jSL#*Pn6}U(=t{>RZf^jh)GzJ(~PwE$%(0<Zi97)xdWK6dc>NkyPL&Vomr|s%Fj5
z@P?8nNFfKp->em%K+~Id<rnC~ofUgr$xo23<HC#<?W{7R)xy%yfVd0cR4n-7_+3YI
z<N5!n1;XD}@;i;o#GeMY{Ow&%4~=`U8*hv;CzR~ojS}tbF!UEN?nJ0L5hYIGuXK^h
zHZDTPR;Ca3P%&@FiQV`yn)y2=$Ky{d;vD96%MIBq=7zb=O6GOr6LwqkVIhk(s=}ZO
z(G{qqvrR|1MMv2Cw35#;i(6^n8_7(7^{1mmAyk}BH`MUIr|aZl%?t*5=sxB%n)$TG
zR2fXOj`7GDtchl($y)CrR7iF$pPmGL>X3icEISQLD=Fo`h<9t{zq;{`!_Nm7_&Eww
z-&fSxsibI8NnbF7+q*&2`$Ofo-MF0UgKyP<2rg#=a-_Mt46I)SSIU~-VraYtq57N>
ztrVzZ!^yKg$ChF|_H>6aBST^E?=Q=|2(H|a50%V`;Md+|AD$FTS?(nxm(Oyr<GhlK
zBX|d<Tyr4r!U*{OJcx~m^~m+;2$W8}q@&)=G_M$%olN6evyx2xK(t-!A?s4I`?A3s
ztz#F{p!i}Du0l$|9^*WIpWeq>uZ19Rp#>MZ*4k^(zt>u+m!iaR)JV7U!1@Wy9u7+R
z7@xqznFHHCt`!PWKe;QTBY7uQ|3OI=*|}_JQQ2w?eK0!lh)VfXNg2tdoBioyPDUh`
zZWxl_En=U+roq)u(+OstUXCScqscqf2DTUq&!s^9AC<_#U;4<r^vCmZaU>s^`weU%
zn$<b*!&kHMXr(9Ik<XM|&EdGU22-p<iv06VS;-*xRpWVH1FeZbdOI0$d5f&mLBD2z
z2O#>(hs?{Wb6U^ql!hEIkdx5(dAfXy81>!LIu6iOlUz$>L}=%Hr5O$yhQFZ5J#m*Z
zFS<a}b$$0dWW0BgrsF6?iwa<!cl1Q8dD|A@r!e*E!zv+)3xQX&>uGMC;fsx;ngvW8
zVqXy+YE|+t95l?ppBS($mCfivC~v(XbECKvhYevinp}z}@nZlf)f>kWR)XfQl~@GP
zS9`+MulLcnPlOyXkY}Up?x8P3yLG%TFr>jiK*wv%ax%@M?0=L*0!(t{H7n70{wMIk
zwGpuL8i==;r_u1dd7o5^mih=G9{Hh`XfntTIwYq3O-UR`?+}(pPsM)kz0Dd&&B$A%
zfj5sEwK61{Z;O%9w3A%F0bnl{)80Qq;L6`UG6?(d@na!fAns;7tomk@Gc20RHxw!G
z>*_H>F+{_y*~K4sv%8R!H0TWjdR&97sX5=jwaZzgITtY8dDFo0i@`tAWfvS|-Y0D^
zI3ZfYc7BV<)X87ohIhayym$8USF{xdiL?HoJ6{FKE)U?^co4q7+gJV&4SnwQmOn<r
zn0tvbN;}rvizCP0d&7EtkD-)BuU`4|FBYvVSy4QAdBKX3;>AT(zDW4yUQ+2^bo*YU
zriz*hYCgyRVfa3y<~DwB_1&T-7{BNFBsEL$1CMVJHMRH|!MBf^WB7XNYog|JeAn`Q
zMol>0>U<70F?g-<^`IsTpNzgoshNRCs&86EDYj4HhWE{(#*53vH-(xqien2lC1uWv
z1<Mx~|JN#v8UM-Sqkz2}td|4za*$pQ(96MjIWR8=g&2&*%fWa#5HAPe<p8|CQWE#F
zu$M)>CHZ|whEkFruJaKiFc{9qYxIlrafQ;u`M5*r;e04c59gzR(!=>UK<VLp{Fc(g
s`M5&q;e136rTlP0`cQtRQC7xNemEaFWO6<hQhF#KzHI-y|DG-W9}92WEdT%j

diff --git a/doc/LectureNotes/_build/html/_sources/schedule.md b/doc/LectureNotes/_build/html/_sources/schedule.md
index 128ec011b..11030f65c 100644
--- a/doc/LectureNotes/_build/html/_sources/schedule.md
+++ b/doc/LectureNotes/_build/html/_sources/schedule.md
@@ -27,7 +27,7 @@ For the reading assignments we use the following abbreviations:
 - Lecture Thursday: Introduction to the course, what is Machine Learning and introduction to Linear Regression.
 - Video of Lecture August 25, 2022 at https://youtu.be/KL0m3-yhd5w
 - Lecture Friday: Basics of Linear Regression
-- Video of Lecture August 26, 2022 at 
+- Video of Lecture August 26, 2022 at https://youtu.be/ne_xCL2ctM0
 
 - Reading recommendations:
   - Refresh linear algebra, GBC chapters 1 and 2.
diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html
index 4f5b3c111..e001a5ed9 100644
--- a/doc/LectureNotes/_build/html/schedule.html
+++ b/doc/LectureNotes/_build/html/schedule.html
@@ -519,7 +519,7 @@ const thebe_selector_output = ".output, .cell_output"
 <li><p>Lecture Thursday: Introduction to the course, what is Machine Learning and introduction to Linear Regression.</p></li>
 <li><p>Video of Lecture August 25, 2022 at <a class="reference external" href="https://youtu.be/KL0m3-yhd5w">https://youtu.be/KL0m3-yhd5w</a></p></li>
 <li><p>Lecture Friday: Basics of Linear Regression</p></li>
-<li><p>Video of Lecture August 26, 2022 at</p></li>
+<li><p>Video of Lecture August 26, 2022 at <a class="reference external" href="https://youtu.be/ne_xCL2ctM0">https://youtu.be/ne_xCL2ctM0</a></p></li>
 <li><p>Reading recommendations:</p>
 <ul>
 <li><p>Refresh linear algebra, GBC chapters 1 and 2.</p></li>
diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index 07bc35051..01ab24bf4 100644
--- a/doc/LectureNotes/_build/html/searchindex.js
+++ b/doc/LectureNotes/_build/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({docnames:["chapter1","chapter10","chapter11","chapter12","chapter13","chapter2","chapter3","chapter4","chapter5","chapter6","chapter7","chapter8","chapter9","chapteroptimization","clustering","intro","linalg","schedule","statistics","teachers","textbooks"],envversion:{"sphinx.domains.c":2,"sphinx.domains.changeset":1,"sphinx.domains.citation":1,"sphinx.domains.cpp":4,"sphinx.domains.index":1,"sphinx.domains.javascript":2,"sphinx.domains.math":2,"sphinx.domains.python":3,"sphinx.domains.rst":2,"sphinx.domains.std":2,"sphinx.ext.intersphinx":1,sphinx:56},filenames:["chapter1.ipynb","chapter10.ipynb","chapter11.ipynb","chapter12.ipynb","chapter13.ipynb","chapter2.ipynb","chapter3.ipynb","chapter4.ipynb","chapter5.ipynb","chapter6.ipynb","chapter7.ipynb","chapter8.ipynb","chapter9.ipynb","chapteroptimization.ipynb","clustering.ipynb","intro.md","linalg.ipynb","schedule.md","statistics.ipynb","teachers.md","textbooks.md"],objects:{},objnames:{},objtypes:{},terms:{"0":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18,19],"00":[0,1,3,5,6,11,18],"000":[1,3],"0000":[],"00000":[],"000000":[5,11],"00000000e":5,"0000e":3,"0001":[1,3],"000148":[],"00019998":5,"00024087":5,"00028369228101198006":[],"00029012":5,"0003043256065368937":9,"00031535148309580783":6,"0003153514830958081":[],"0003153514830958126":[],"0003153514830958235":6,"00034944":5,"000417932":2,"00042089":5,"000464088":2,"00050694":5,"00058016":6,"000599":[],"00060705":[],"00060708":6,"00061058":5,"00062582":6,"00062595":[],"00066668":6,"00068734":6,"00068941":6,"00069103":[],"00073541":5,"00076495":6,"00076919":6,"00076925":[],"0007698473260556325":6,"0007698473260556334":[],"0007698473260556339":[],"0007698473260556344":6,"00079123":[],"00079130":6,"00079968":5,"00084705":[],"00084711":6,"00085883":6,"00085884":[],"00087697":6,"00088573":5,"00092645":6,"00092646":[],"00096314":5,"001":[1,2,8,13],"00100519":6,"0010479245355337968":[],"0010479245887943952":6,"00105081":6,"00106677":5,"00107405":6,"00111756":6,"0011526":6,"00115999":5,"0011828302640124":[],"00118504":6,"00118527":[],"001263":[],"00128479":5,"001323":6,"00137823":6,"00137835":[],"00139705":5,"00149311":6,"00149956":6,"00152117":6,"00154733":5,"00156379":[],"00156382":6,"00168251":5,"00174276":6,"00175331":6,"00186347":5,"001988":[],"00198806":[],"00200":8,"00202624":5,"00202756":6,"00217499":6,"00224413":5,"00228741":6,"0023548":6,"002381316302584885":[],"002381316302584886":6,"0023813163025848865":6,"00242999":6,"00243186":6,"0024401":5,"00249435":6,"002526":[],"00266858":2,"00270244":5,"00274989":6,"00275135":[],"00289724":6,"00293838":5,"002948":[],"0030828":[],"003083":[],"00310113":2,"00312361":6,"00315593":6,"003215318065760509":[],"0032153180657605125":6,"00323332":6,"0032542":5,"003301":6,"003395515459204093":6,"0033955154592040944":[],"00353823":5,"0036237":6,"0036367":6,"00369758":6,"003704":[],"003717":[],"003759":[],"003774":[],"003788":[],"0038335":6,"00390021":[],"003909404072811217":6,"003909404072811231":[],"00391839":5,"0039987":6,"004":5,"004091940707753948":6,"0040919407077539514":[],"00410387":6,"00410478":6,"004113634617443116":6,"0041136346174431284":[],"004113634617443131":6,"004113634617443135":[],"00411363461744314":6,"004113634617443141":[],"004155986345861364":6,"004155986345861374":[],"00415763":[],"00424909":2,"00424967":6,"00426027":5,"00433417":11,"00440346":6,"00443743":6,"00445655":11,"004579219539673834":6,"004579219539673836":[],"00458878":6,"004610275230656187":6,"004610275230656294":[],"00462287":6,"00471782":5,"00472199":6,"00472512":6,"0049544":6,"004999999999999984":[],"004999999999999994":[],"004999999999999996":0,"00512927":5,"00517114":6,"00526348":6,"0053018":6,"00554552":6,"00556826":6,"0056799":5,"00577441":[],"00579953":6,"00588657":6,"00607783":6,"006162":6,"00617499":5,"00630331":6,"00642221":6,"00660427":6,"00672607":6,"00673393":13,"00673407":6,"00676387":6,"0068011":6,"00683748":5,"00683964":6,"007012403613997257":[],"007024126888936694":[],"0070241268889382116":6,"00719176":6,"00727646693":0,"007315":[],"0074331":5,"00751823":[],"00759119":6,"00784393":6,"00803064":6,"0080866254785146":[],"00813803":6,"00817631":6,"00823002":5,"00827728":6,"00831018":6,"00834567":6,"00848904":6,"0086649156":0,"008675369724975977":5,"008675369724976501":[],"00894639":5,"00905423":6,"009163470508352211":[],"009163470508352218":5,"009164545680330616":6,"00917248":6,"00934499":6,"009450756365829578":[],"0096208":6,"00990475":5,"00992331":6,"00996754":6,"00it":6,"01":[0,1,2,5,6,9,11,13,18,20],"010018312644139205":6,"010018312644139347":[],"0100706":6,"010331721306655144":6,"01033172130665515":[],"010516485576646513":6,"010516485576652856":[],"01066519":6,"01076611":5,"0110":18,"0110407067093945":[],"01104071":[],"011076219011339788":[],"01107621901137465":6,"011225":2,"0113104":6,"01179792":6,"01191824":5,"01199624e":18,"012073649439965807":[],"012073649472576395":6,"01212754811385597":18,"01219292":6,"01223198":6,"01231917":6,"012633802944855959":[],"01290947":6,"01295356":5,"013121573975499602":[],"013121573975499604":[],"01312157406137079":6,"013121574061370796":6,"01318643":6,"01347916":6,"01348565":6,"01367553":6,"01397146":6,"01405935":6,"01416528":6,"014209325470380275":[],"01433809":5,"014436800088896274":6,"014436800088969727":[],"01449782":6,"01458337":6,"0146081":6,"01463049":6,"015072388895177088":[],"0150723888951771":6,"015072388895177109":[],"015072388895177239":6,"01509543":13,"01530715721129232":[],"01531845":6,"01538461538462":[],"01549377":6,"01558197":5,"016285782696017055":6,"016285782696075054":[],"01633913":6,"01640891":6,"01655318":6,"016587414993037307":[],"016587414993045405":6,"01691985":6,"01708781":6,"01708852":6,"01713366":6,"01724499":5,"01735584819559184":[],"017355848195591845":[],"017355848195593354":6,"01756972":[],"01799917e":18,"01817152":[],"018232":[],"01831036e":6,"01831130e":[],"01831200e":[],"01831207e":6,"01866537":6,"01869785e":18,"01873344":11,"01873869":5,"01898855":6,"01905883":6,"01908936":6,"019587":[],"01963611":6,"01969145":6,"019754165271682844":6,"01975416527179247":[],"01975848":6,"02":[0,4,6,7,12,18],"02024962":6,"02054837e":6,"02068067":6,"02073509":5,"0209518407597062":18,"02098261":6,"02123176":6,"021250026482402":11,"02159270458799264":[],"021592704587992645":[],"021592704588021164":6,"021592704588021167":6,"02198702e":6,"022022882954618364":[],"02208512":6,"02228115":6,"02229529":6,"02252765":5,"022556":11,"02255619":11,"02299949826036602":[],"0229994982603662":6,"02348765":6,"02365049":6,"0245528":6,"02492265":5,"02493054":[],"02498832":6,"02503753":6,"02511518":6,"02522069":6,"025709":11,"02586427":6,"0260906":6,"02625193":8,"02625928":13,"026408391362671896":[],"02642055e":18,"026605727637176654":[],"02660572763718461":6,"026605727637184613":6,"02707227":5,"02723445":6,"027609773491022314":[],"027609773491022387":6,"027609773491022407":6,"02786488":11,"027865":11,"02857":4,"02917662":11,"029177":11,"02926396":[],"029613363972682966":[],"029733":[],"02976145":6,"02994311":5,"02f":6,"03":[1,6],"03032441e":6,"03056589":[],"030566":[],"03071040e":18,"03076923076924":[],"03077640549":4,"03099776":5,"031":5,"03187339273559067":[],"03196357":6,"03251863":5,"03256632e":1,"03267527":6,"03278964":[],"032790":[],"03279636":6,"0330308045180234":[],"0330308045183163":[],"0330308045183219":6,"0330308045190182":6,"033657685071527485":[],"033657685071527624":6,"03382304823545749":11,"03447512":6,"03562355":6,"03568439":6,"0358909447132981":[],"0359565":5,"03630548":6,"03707133":11,"037559":11,"03755944":11,"03761519":[],"03781367141738885":[],"03781367141738886":[],"037813671417388985":6,"03781367141738899":6,"03794112":16,"03814292":6,"03815288":6,"038300":11,"03850557":11,"038506":11,"039039":5,"03981057":16,"0399676689527968":6,"0399676689527975":6,"04":[1,6,11,18],"04010697":6,"04063602":6,"041":[],"041148729430502":6,"041148729430523":6,"0411487294305246":[],"0411487294305746":[],"04179719":[],"041797190646905":[],"04220758":6,"04284519":[],"043":[],"04315108":5,"0433816":[],"04346721":5,"04355837":6,"04362":[],"04368707":16,"0437499":2,"04389027":6,"043927769551648":[],"04423486":6,"044334":[],"0444119":[],"044613":6,"04483457":[],"04537385":6,"04543942":6,"04566964":6,"0458":9,"04615384615386":[],"04621521":[],"04648335":5,"0466":[],"04683565":5,"04775904":16,"04784395":6,"04818727730430296":[],"048187277304303056":6,"048653428302840175":16,"04892055":6,"04909093":6,"04912436":6,"04930820e":18,"0495569966278238":6,"0495569966278269":6,"0495569966278315":[],"04it":[],"05":[1,3,4,6,13],"05009826":6,"05087958":[],"05100875":6,"051649":11,"0517473":5,"05227921801205691":[],"05227921801205692":[],"052279218012057004":6,"05255759":[],"05263":[],"05290417684691035":[],"05302":[],"05364854":8,"05383795":6,"05434571":16,"05447415":6,"054585":[],"054785":[],"054963":[],"05505310046362":[],"05505310046363":2,"055137":[],"055320":[],"05533":[],"055676":[],"05614483":5,"05623":[],"05648":[],"05651951":6,"056528":11,"05667":[],"057088709963182":[],"057163681553428394":6,"05716368155351588":[],"0572":[],"05756733":[],"05785343":6,"05789007":6,"057899":11,"05796251":6,"05807125":6,"05883":[],"05884":[],"059013":5,"059294":11,"059378":11,"059601":5,"059783":[],"05999":[],"06":[6,13],"060080":[],"060183":[],"06020587":6,"06021285":[],"060213":[],"060228":11,"060309":5,"06043581":6,"060507":[],"060655":[],"060781":[],"060841":[],"061025":[],"061048":[],"061149":5,"061321":[],"061390":[],"061509":[],"061604":[],"06160438":[],"061701":[],"061745":[],"061898":[],"061917":[],"061923":11,"06200174":5,"06208238634231944":6,"06208238634231953":[],"062136":5,"062142":[],"062292565":4,"062294":[],"062391":[],"062409":11,"062411":11,"062435":[],"062451":[],"062534":[],"062676":11,"062706":11,"062834":[],"062884":[],"063052":5,"063078":[],"063250":[],"063343":[],"063364":5,"063513":[],"063631":5,"063657":[],"063752":11,"063851":5,"063872":[],"063874":5,"06388888888888888":[],"064052":[],"064108":[],"064110":5,"064184":[],"064274":5,"064384":[],"06444":[],"064469":[],"06453579006728315":[],"06453579006728322":6,"064538":11,"064604":11,"064648":[],"064658":5,"064681":[],"064838":5,"06491736":6,"064918":[],"064931":11,"064964":[],"06497046":[],"065020":5,"065106":[],"065249":11,"065314":11,"065348":5,"06547790180152352":6,"06547790180152353":6,"06547790180152357":[],"06547790180152363":[],"065514":[],"065557":[],"065575":[],"065641":11,"065730":11,"065826":5,"065892":[],"065974":5,"06602663":[],"066110":[],"066194":[],"066309":[],"06637":[],"066449":5,"066558":5,"066609":[],"066670":11,"0666807":2,"066729":11,"066753":[],"066789":5,"066837":11,"066954":[],"067079":[],"067213":5,"067236":[],"067240":5,"06724062":5,"067282":11,"067392":11,"067402":11,"067432":11,"067544":[],"067597":5,"067599":5,"067612":5,"067620":5,"067622":5,"067675":[],"067714":[],"067723":5,"067769":5,"067956":11,"068059":11,"068093":11,"068094":[],"068103":[],"068120":[],"068318":5,"068363":5,"068376":5,"06844519414009438":[],"06844519414009442":6,"06844519414009444":6,"068461":11,"068538":11,"06858699":[],"068608":5,"068642":5,"068659":[],"068735":5,"068806":5,"068809":[],"068831":[],"068937":[],"068965":5,"068991":5,"068992":5,"069085":11,"069091":[],"069181":5,"069275":11,"069408":5,"069409":5,"069441":5,"069455":[],"069584":[],"069672":[],"069685":11,"069754":[],"069796":5,"069847":5,"069877":11,"069884":11,"069888":5,"069912":[],"06it":6,"07":6,"070028":[],"070043":[],"070071":[],"070074":5,"070150":5,"07016":[],"070163":11,"07017":[],"070343":11,"070379":[],"07039":[],"070419":11,"070429":11,"070491":5,"070492":11,"070576":[],"070612":[],"07062318":6,"070630":5,"070635":[],"070889":11,"070911":[],"070950":11,"070972":11,"07099747918547346":[],"071022":11,"071086":[],"071112":5,"071136":5,"07115":[],"07129539":[],"0712953943627344":[],"0713":0,"071387":[],"07145103":11,"071467":5,"0714956":16,"071529":5,"07160048164232467":6,"07160048164248561":[],"071614":5,"071632":5,"071698":[],"071705":[],"071767":5,"071921":5,"071935":[],"07201957":[],"072216":11,"072242":[],"072348":[],"072442":11,"072492":[],"072530":[],"072617":[],"072620":[],"072750":[],"07285":3,"0728785":[],"07291729":18,"072928":[],"073016":[],"073062":11,"073136":11,"073225":[],"073230":11,"073280":11,"073362":[],"073368":[],"073423":[],"073436":11,"073449":5,"07345504":[],"073531":[],"073575":11,"073583":[5,11],"073598":11,"073629":11,"073695":[],"073699":5,"073719":5,"073761":11,"073810":11,"073816":5,"073827":11,"073907":[],"073949":5,"074034":5,"074067":11,"074111":11,"07413172":[],"074149":11,"07421084":5,"074237":5,"074336":[],"074375":5,"074488":[],"074530":11,"07456491":5,"074573":11,"074666":[],"074693":5,"07490892":6,"074945":[],"075195":5,"075241":5,"075573":[],"075594":5,"075760":5,"075820":[],"075959":5,"076130":11,"076204":[],"076216":11,"076338":5,"0764924":6,"07656896":[],"076589":5,"076604":11,"076628":5,"07678":[],"076780":11,"076804":11,"076895":11,"076905":11,"076938":11,"076955":5,"077003":5,"077013":11,"077015":11,"077022":[],"077083":11,"077211":11,"077349":11,"07735703":16,"077402":5,"077425":5,"077452":11,"077467":[],"07777777777777778":1,"077793":5,"078040":5,"0782":6,"07820":[],"078253":[],"078254":11,"078269":11,"078299":11,"078314":11,"078436":11,"078548":[],"07864":[],"078667":[],"07871":[],"07878641":16,"078911":[],"078992":[],"07903849":16,"079157":5,"079281":5,"079347":[],"079383":5,"079389":5,"079405":5,"079406":[],"07944154":16,"07968918676726029":[],"0796891867672603":6,"079729":[],"079839":[],"079847":[],"079893":[],"07989327":[],"07999999999998":[],"08":[13,18],"080106":[],"080137":[],"080256":[],"080297":5,"080312":[],"080319":5,"080334":[],"080347":[],"080370":[],"080406":[],"080410":[],"08041015":[],"08043851":5,"080517":[],"080582":11,"080593":11,"080647":11,"080706":11,"080738":11,"080801":[],"080847":5,"08085812":[],"080916":[],"080984":11,"081046":11,"081072":11,"081140":[],"081144":11,"081233":5,"081253":5,"08131003":6,"081388":11,"081431":[],"081483":[],"081533":5,"081540":[],"08156108":6,"081773":[],"082174":[],"082183":[],"082295":11,"0823185":[],"082451":[],"082503":11,"08251519":6,"082536":11,"082896":11,"082900":[],"08299273e":6,"083015":5,"083053":[],"08318298e":1,"083220":[],"083227":[],"083276":11,"08328216846752691":11,"08333333333333333":1,"08336233266":4,"083404":[],"083441":[],"083506":[],"083527":[],"083600":[],"083692":[],"08376632":6,"083766322923899":6,"0837663229239016":[],"0837663229239025":[],"0837663229239043":6,"083799":[],"083829":[],"083977":[],"084000":[],"084101":[],"084184":[],"084224":[],"084226":[],"08426840630693411":[],"08426840630693412":6,"08426840630693413":6,"08428156":16,"084282":[],"084364":[],"084400":[],"084408":[],"084414":5,"084444":[],"084484":11,"084536":[],"084549":[],"08455":[],"084570":[],"084604":[],"084629":[],"08464758160254343":5,"084670":[],"084683":[],"084702":5,"08474":[],"084777":[],"084862":[5,11],"084904":5,"084909":5,"084965":[],"085018":5,"085044":[],"085163":11,"085167":[],"085184":[],"085185":[],"085361":[],"085416":[],"08551306":6,"085764":[],"08576932":6,"085879":11,"08593216":6,"086074":11,"086076":5,"08611111111111111":1,"086112":11,"086303":[],"08630331":[],"086567":[],"08673755293381497":[],"086807":5,"086872":5,"086890":11,"08690":[],"086956":11,"087501":[],"08758":[],"08759":[],"087834":[],"08816688":[],"088176":[],"0881981":5,"088212":5,"088526":11,"088611":[],"088631":[],"08871404":5,"088809":[],"088816":[],"088825":[],"088853":[],"08888888888888889":1,"088926":[],"08902":[],"089059":[],"08917679":5,"089177":5,"089212":[],"089329":[],"089425":[],"089501":11,"089614":[],"0896981":6,"08996":[],"09":1,"090028":[],"090229":[],"09085624":16,"090929":[],"091051":11,"091060":[],"09117221":[],"091224":[],"091236":[],"091340":[],"091349":[],"091363":[],"091414":[],"091416":[],"091426":[],"09149881":[],"091630":[],"09166666666666666":1,"091696":11,"0917":9,"09170751":[],"09172408":6,"091891":[],"092066":16,"09216046":[],"0923":[],"09251":[],"09297039":[],"093247":[],"0934597075922044":[],"0938":[],"094082198961999e":6,"0940821989624095e":[],"094082198966615e":6,"0940821989673748e":[],"09440475":18,"094404754965417":18,"09444444444444444":1,"094472507965532":11,"095510":[],"095702":[],"095871":[],"09609807":5,"096173":[],"096472":11,"096623":[],"09672929714683368":[],"097360":[],"09744":[],"09744272":6,"09780":[],"09791":[],"09849763":11,"098498":11,"09856879":16,"09861229":16,"098802859381565":16,"099":[],"09903804":8,"0991919894927399":6,"099191989493334":[],"09951287404314545":1,"0n":0,"0s":[3,4],"0x11da42d90":13,"0x11e031e50":13,"0x7fad10f9a280":[],"0x7fad20f69be0":[],"0x7fd098df3280":[],"0x7fd0a9063be0":[],"1":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,17,18,19,20],"10":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,16,18],"100":[0,1,2,3,4,5,6,7,8,9,10,11,13,14,16,18,19],"1000":[0,1,2,4,5,8,11,13,14,15,18],"10000":[2,5,6,10,11,18],"100000":8,"10001":10,"1001":18,"1002":18,"1003":18,"10030":[],"10044225464078282":[],"1005":18,"10077114273548984":6,"1009":18,"1011":18,"1013":18,"1013904243":18,"10141413e":6,"1015":18,"102":3,"1022233262115424":[],"10222333":[],"1023":18,"10230":[],"1023111":16,"1024":3,"1026":18,"1027":18,"1028":[],"103":1,"1030":18,"10302062":16,"10340":[],"1037":18,"10378326e":1,"1038":18,"10391807":6,"10398646080125035":[],"10398646080125036":6,"10398646080125037":6,"1040":18,"10405456":11,"10430":[],"104411":[],"10455924":[],"1047":18,"10505137":16,"10520":[],"10555555555555556":1,"1056":[],"10589577":5,"106":[],"106095":11,"10620135":16,"108":6,"109":[],"10913":6,"10931453":6,"10960":[],"1096767776832326":18,"10967678":18,"10th":9,"10x":0,"11":[0,2,3,4,5,6,7,8,9,10,11,12,13,16,18,20],"110":[],"1100":18,"1101":18,"11022302e":5,"11046771":16,"111":[1,7,12],"11100":[],"1122558214":[],"112383":11,"1124":[],"1125":3,"11297834":[],"1136":3,"11362930e":[],"11388888888888889":1,"1139":3,"11390":[],"114":3,"11402309":16,"114437":[],"11462415":5,"11482289e":6,"11499517":[],"11507992e":1,"11547777218875695":6,"11547777218875696":6,"11547777218940905":[],"11547777218940906":[],"115822":6,"11590":[],"11598862":18,"11598862273198":18,"11604844":[],"116048442683864":[],"11660":[],"11666666666666667":1,"11674401539815678":18,"117":8,"11744554e":6,"11780":[],"11790868":[],"117986":[],"118":2,"1182":[],"1183":[],"118318":[],"1184":4,"11840":[],"1185":[],"1186":[],"11890":[],"119":2,"1199":[],"119936":2,"119999999999976":[],"11it":6,"12":[0,1,2,3,4,5,6,8,9,11,12,13,16,18,20],"120":[2,3],"1203":[],"1203284":8,"1206":8,"12069773":16,"121":[2,8,9,10],"12182967":6,"12196674":[],"122":[2,8,9,10],"12222222222222222":1,"122439":[],"1224392":[],"12288563":5,"122886":5,"123":[],"12318726e":6,"12333649":6,"12345260617257443":9,"123459876":[],"123711":6,"12380":[],"124":0,"12400":[],"125":[],"1250":[],"125000":[],"12552073e":6,"126":[],"1261":[],"12618549":5,"1265":[],"127":4,"1270":[],"127043":[],"1271":6,"1277":6,"127773":[],"12777777777777777":1,"12788968":[],"12790":[],"128":[3,4,13],"128664":6,"12898627064868978":[],"12937662":[],"12945452":[],"1297":[],"1298":9,"12adb44b1c20":7,"12m":3,"13":[0,2,4,5,6,9,11,12,13,16,18],"130":[],"13003291":6,"13055555555555556":1,"131":[],"13117061":0,"1314":[],"132":[],"13220608e":6,"13229545716505003":16,"132360":[],"1323603":[],"1326":[],"13280":[],"133":7,"13333333333333333":[],"135":[],"13519106":[],"13535942":6,"136":[],"1360":[],"13609760e":18,"1361":[],"1362":[],"1363":[],"1364":[],"13646574":5,"13661243e":6,"13679863":6,"1371":6,"13740":[],"137400784702911":[],"137652":11,"1378":[],"1382":[3,4],"1383":[3,4],"1384":[3,4],"1385":[3,4],"1386":[3,4],"13865173":5,"13876586436927824":5,"138775":11,"1388888888888889":1,"13890":[],"1392559581788775e":[],"1392559584983597e":[],"1392559585048734e":6,"13925595925919e":6,"14":[0,2,4,5,6,8,9,10,11,12,13,16,18,20],"140":[],"14021063":6,"1404":[],"14042769":[],"140428":[],"141":2,"14100":[],"1416398":6,"14174745":6,"1418":[],"142":2,"14250":[],"142857":[],"143":[2,7],"1435666":[],"14360598":[],"1437":1,"144":2,"14400":[],"1440501043841336":1,"14440":[],"1446729567":4,"14484695":16,"145":2,"146":[],"147":[],"14710":[],"14722222222222223":1,"147400":11,"14741468":[],"147420":11,"14783702":[],"1479":[],"148":[3,4],"14812206":6,"14818":[],"1484256":[],"148564":6,"14859":6,"148768":[],"149":[3,4],"149213":6,"14932651":[],"14988578":[],"149886":[],"14g":6,"14it":6,"15":[0,2,4,6,7,8,9,12,13,16,18],"150":[3,4,8],"15005476":5,"15098090e":6,"151":[3,4],"15130074e":6,"151986":[],"152":[3,4],"15200":[],"15258907":[],"1527777777777778":1,"153036":[],"153106":[],"1533795":[],"153760":[],"15384615384616":[],"154":[],"15454301":[],"154720":[],"15483121":[],"154911":[],"155":[],"155491":[],"155687":[],"155883":[],"156":[],"1562":[],"15629539":[],"15680777":16,"157":[],"15717291":16,"1575":[],"158":[],"1583767":16,"15843769515580663":18,"1586300629904382":[],"1587":[],"15891336":16,"159":[],"1590":[],"15990":[],"15g":6,"15it":[],"16":[1,2,3,4,5,6,8,9,10,13,18],"160":[],"1603":3,"1604":[],"1605":[],"160539":[],"1606":[],"1607":[],"16111111111111112":1,"1612":[],"16211139":5,"16220":[],"16231451":4,"1625":[],"1628":[],"163":[],"1630775253":[],"16342407":5,"16343471":6,"16384":3,"16500":[],"166":[],"16660817":[],"166667":[],"167":[],"16740002":16,"168":[],"16805821e":6,"16807":[],"16827044":[],"16832385":[],"16b8e3cda33a":1,"17":[1,2,4,5,6,8,13,18],"17084902":[],"1709":[],"17174962e":1,"17222222222222222":1,"1726":[],"17300":[],"1731":[],"17339342":[],"17446471":6,"1752":[],"175300":[],"17641709":6,"176880142835407":[],"17777777777777778":1,"17801022":5,"17861098":6,"17917768":5,"17930649":16,"17949575":5,"17953942":11,"1797":[1,3],"18":[2,4,6,7,8,9,10,13,18],"180":[],"18029127":5,"1807":4,"1809":[],"181":[],"1810":4,"1812":[],"18156717":16,"1821":[],"18276924":[],"18327677":[],"18333333333333332":1,"18393678":[],"184":[],"184519":[],"184895":[],"185":[],"1851":[3,4],"1852":[3,4],"18526":[],"185278747229417":[],"1853":[3,4],"1854":[3,4],"1855":[3,4],"186":[],"1860":[],"1861":[],"18611111111111112":1,"18613217e":6,"18660":[],"18673098":11,"18682538":[],"1887":6,"18912963":16,"189496":[],"189622":[],"18it":[],"19":[2,4,6,13,18],"19003":6,"191262820314401":[],"19166666666666668":1,"19207979":5,"19213479":[],"19220":[],"193":[],"19354258":[],"19379506":[],"19394283":[],"194":[],"1940":0,"19404282648955e":6,"194042826653172e":[],"194042826815498e":[],"1940428268204826e":6,"1943":12,"1944":[],"19444444444444445":[],"19466812":[],"1950915150":[],"1954":[],"1956":[],"19569961":6,"1961":[],"1962":[],"1963":[],"1964":[],"1965":[],"1970":16,"1973":9,"197370":11,"19740":[],"1979":6,"19800":[],"19853775e":18,"1989":[],"199":[],"19937":[],"19994371":6,"1_1":12,"1_2":12,"1_3":12,"1cm":[0,8,10,18],"1d":[1,2,3],"1e":[1,2,3,4,14],"1e10":14,"1e4":6,"1f":1,"1k":16,"1n":0,"1s":3,"1x":0,"2":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,20],"20":[0,1,2,4,6,7,8,13,18],"200":[0,2,3,4,8,9,10],"2000":0,"200000":[],"20015436":6,"2004":13,"2006":20,"2010":1,"2011":1,"2014":4,"2015":1,"2016":0,"20174121":16,"2018":[0,6],"2018906331":[],"2019":[],"2021":[6,14],"2022":17,"2027":[],"20277777777777778":1,"203":6,"20320502":[],"20375361":[],"203753611274545":[],"20500":[],"20509391":[],"206":[],"2060":[],"20661216":[],"2069":[],"207545":11,"20756638":[],"20833333333333334":1,"20867052175006306":6,"20867052175109335":[],"20980":[],"21":[0,1,2,4,5,6,7,9,12,13,16,18],"210340":11,"21058097":5,"21130":[],"2116753732":4,"21169159e":6,"21265216":[],"213":6,"213103":11,"213743":11,"21472683":16,"2147483647":[],"21519063":16,"21596432":6,"216290":11,"216683":11,"21786964":16,"2188":[],"21919813":[],"21944444444444444":[],"2195":3,"21985165":13,"21997099":16,"22":[0,1,2,4,5,6,12,13,16],"22044605e":5,"220506557673408":18,"22094791":[],"221":8,"221180":[],"2216":[],"2218":[],"221805":2,"222400":[],"2225":3,"225":4,"2259440937":[],"226124657522696":[],"22612466":[],"22690428":5,"2284246870217459":6,"22842468702288576":[],"22842468702288582":[],"22847924":5,"22929905e":18,"2299":3,"22it":6,"23":[1,2,4,6,7,12,13,16],"23002365e":6,"23014274e":18,"231":[],"23103285":[],"2315033":[],"23167717":5,"2321528":5,"232153":5,"232435":[],"23297056":[],"23333333333333334":1,"233528":5,"234":6,"23522201":[],"2357089093":[],"2360682191515046":[],"2361111111111111":[],"2364":[],"237038":5,"23703839":5,"2379":6,"2397":[],"24":[0,1,2,4,6,13,16],"24140":[],"2416":[],"24175744e":6,"2419":[],"24276315":16,"2430":[],"24390":[],"24444444444444444":1,"244858":[],"24485843":[],"24512498":16,"246":2,"2470":[],"24770094":6,"24829908":5,"24906604e":6,"24924624":[],"2495":[],"25":[2,3,4,5,6,7,8,9,11,13],"250":[2,4,7,9],"2500":[],"25000":0,"250000":[],"250154":[],"2517560119":[],"251879":[],"252436":[],"25259666":16,"2526":3,"252866":[],"25286618":[],"253775":[],"255":3,"255001":[],"256":[2,4],"25617654e":6,"25617657e":[],"25617658e":6,"256962":[],"257":[],"2572":[],"2572495066":[],"2572e3a4b38d":1,"2575":[],"25792767":16,"25845e8df859":6,"259153":11,"2597":[],"25976336":[],"26":[2,4,6,13],"26079358":[],"26153846153846":[],"26186844":16,"2619":[],"26290036":[],"26294938":[],"2629493813057833":[],"26301436":5,"26306244":[],"264":4,"26409315307910025":6,"2640931530791003":[],"2640931530791005":[],"26409315307910053":6,"265":[],"2650":[],"265109911":4,"26531223e":18,"2654":[],"266":[],"26610075":[],"26666667":13,"26710969":5,"26780278":5,"268":[],"268227":[],"26822717":[],"269":[],"27":[0,1,2,4,6,13],"270":[],"2703":3,"27092897":6,"2750":[],"275341":[],"27562809e":18,"27621662e":18,"276263":11,"27692307692308":[],"27700":[],"2772":3,"2775623201":[],"27760":[],"278036":5,"27803645":5,"27n_":18,"28":[1,3,4,6],"28001319":[],"28047021":16,"280647":11,"28067036":16,"28097861":5,"280979":5,"2812":[],"282727":11,"283":[],"2830637392":4,"28336218e":6,"2836":[],"28390":[],"28475098":8,"28590743":[],"2861":18,"28634473":11,"286345":11,"2871":[],"2873":9,"2882":18,"2886":18,"2886847885377843":[],"28868479":[],"28875373":16,"2890":0,"2892":18,"28967287":16,"29":[4,6,7],"29149329":[],"2915":18,"29167186":5,"29229741":13,"2923076923077":[],"29512284":11,"295123":11,"2954":[3,4],"2955":[3,4],"2956":[3,4],"2957":[3,4],"2958":[3,4],"29588901":13,"29592539":[],"296247":[],"297":[],"2971492148":[],"2972":[],"29822833":6,"298273":[],"298375":[],"299267190588216":11,"299748":[],"2_":12,"2_1":12,"2_2":12,"2_3":12,"2_i":12,"2_m":[6,18],"2_t":13,"2_x":18,"2b":18,"2cm":8,"2d":[1,3,11,12,15],"2e":6,"2f":[0,7,9,10,11,12],"2ff97f4bf03b":[],"2g":2,"2g_i":2,"2k":3,"2m":6,"2n":[0,2,3],"2nd":9,"2p":18,"2pt":4,"2s":4,"2x":[0,3,8,13],"2x_ix_jy_iy_j":8,"2x_j":8,"2y_i":10,"2y_j":8,"3":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19],"30":[0,1,4,6,7,10,13],"30000":0,"30010":[],"30119421":8,"3017":[],"3018":[],"3019":[],"301927":[],"3020":[],"3021":[],"30258509":16,"302m":4,"303":6,"3037":[],"3038":[],"30384239":16,"3039":[],"303m":4,"3040":[],"3041":[],"30466214e":6,"304m":4,"30535506":[],"30552077":16,"30559547":[],"305m":4,"3063":[],"306m":4,"3072":3,"30761405e":18,"30787294":6,"307m":4,"308m":4,"309":0,"30940":[],"30b53504a633":0,"31":[4,6,12,16,18],"310":0,"311":0,"311m":4,"312":0,"3120271598582915":18,"3123314713548606":6,"31276579e":6,"312m":4,"313":0,"31318084":5,"314":[],"31457796":5,"315":6,"3155":[5,6],"316":[],"31605061":16,"31614188e":18,"317":[],"31718909":11,"317367":11,"317m":4,"31866499":13,"31896852":8,"319m":4,"32":[3,4,6,12,13,16,18],"3200":1,"32039227e":18,"32047562":[],"3208":2,"320m":4,"32149601703519115":6,"3214960170351912":6,"3215":[],"32179365":[],"32309075":[],"32320052":16,"324":2,"32458459":[],"324m":4,"3250":[1,6],"32512":[],"326238":[],"326m":4,"32721178":11,"327212":11,"32750531e":18,"32816737":[],"32903042":[],"3297":[],"32992274":[],"329923":[],"33":[3,4,12,14,16],"3303":[],"33066907e":5,"3310":[],"33166055e":5,"331939":[],"331m":4,"332331":[],"333":7,"3331":[],"33327369":[],"333274":[],"33333333":13,"3337":[],"33611111111111114":[],"33711888":[],"3384":[],"33900713":[],"33m":4,"34":[4,7,16],"3403":[],"34040204e":18,"340782":11,"34114547":5,"341m":4,"342680":[],"34305928":[],"3436":0,"3437":0,"3456":[],"34568872":16,"34569596":5,"3457":[],"3458":[],"34585355":16,"3459":[],"3460":[],"346810":[],"3469819128513336":[],"34740615":[],"3498":[],"34it":6,"35":[2,4,6,7],"3514":[],"35140":[],"35147135":16,"351636":11,"35182854":5,"35203688":16,"3522":[],"3525":[],"3528556":[],"352856":[],"3529":[],"3530606977":[],"3538":[],"35396404e":18,"35401056e":18,"3543":[],"3544313922":[],"3546":[],"35470445e":5,"3548":[],"355":[],"3551":[],"35533773":6,"35533774":[],"3556":[],"35562617e":18,"3558":[],"3560":[],"356399":[],"3567":[],"3572":[],"35724288e":18,"3575":[],"357508":[],"3576":[],"3577":[],"35771842":6,"3581341341":4,"3585":[],"3587":[],"35892474":16,"359":5,"3593":[],"3594821":13,"3595":[],"3598":[],"359999999999985":[],"36":[0,2,4,5,6,7,18],"360":1,"3604":[],"3605":[],"3606":[],"360688":[],"3609":[],"36102113":[],"36117602":[],"3613":[],"3615":[],"361556":[],"3617":[],"3621311":5,"3624":[],"3627":[],"3628":[],"363295916323784e":6,"363295916414895e":[],"3632959170548605e":[],"363295924430451e":6,"363834":11,"36383443":11,"3643":[],"364418e97433":1,"3645":[],"3646":[],"3647":[],"3655":[],"3655222":5,"3659":[],"3668":[],"36689784":[],"366898":[],"3669":[],"367":2,"3672":[],"3673":[],"3674":[],"3679":[],"3684":[],"3687":[],"3688":[],"368m":4,"369139":11,"36it":[],"37":[2,4,6,7],"3704":[],"370782966":4,"3711":[],"3713":[],"3718":[],"3721":[],"3722":[],"3725":[],"3729":[],"37307168":16,"3739":[],"37396662":6,"37415316":16,"374291":5,"37429133":5,"3748":[],"3749":[],"3753":[],"3759":[],"3760":[],"3765":[],"376559":[],"37655936":[],"37692363":[],"37703055":[],"3772":[],"3773":[],"37732":[],"3776":[],"3777801602":[],"3779":[],"37835429e":[],"3784":[],"37900111":6,"3791":[],"3794":[],"379647":11,"37964744":11,"37992857":[],"38":[2,4,7,18],"380":[],"3802":[],"3803":[],"3804":[],"3805":[],"380739":5,"38073947":5,"3811":[],"38135733e":6,"3815":4,"381627865854956":[],"38162787":[],"3817475779":[],"3818":[],"3819":[],"3820":[],"38246359":[],"3827":[],"382951":11,"38295101":11,"3830":4,"3833":[],"38336316":16,"3834":4,"3837":4,"3838":4,"3839":4,"3842":[],"3850":[],"3851":4,"38533184":6,"3854":[],"3858":4,"386":[],"3861":4,"3862":4,"38629436":16,"3864":4,"3867":[],"3869":4,"387":[],"3871":4,"3872":4,"387482":5,"38748219":5,"3876":[],"388":[],"3881":4,"3882":[],"3885":[],"38868469":16,"3888":4,"3889":4,"389":[],"38901478":[],"3891":4,"38916861e":6,"38962192e":6,"3898":[],"39":[0,2,4,13,19],"390":[],"3906":4,"39095416":[],"3914":4,"3915":4,"3916":4,"391602":[],"3917":4,"3922":4,"3925515884752442":[],"3928":4,"3931":[],"39311435":16,"3943":4,"3944":4,"39456996":16,"3950":4,"3955":4,"39560937":16,"39579407":5,"3958":[],"3960":4,"3962":4,"3970":4,"39706038":5,"39724390e":18,"3975":4,"397700":11,"39789527":16,"3979":4,"39794864e":18,"3980313467":[],"3983":4,"3994":4,"3996":4,"399836":[],"3d":[2,3,4,6,13],"3f":[1,3,9],"3n":16,"3x":[2,8],"3x_i":2,"3y":8,"4":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],"40":[1,4,6,19],"400":4,"4000":20,"4005":4,"4011":4,"40116777":[],"401168":[],"4012":4,"4017":4,"401842":11,"40214433":16,"4022":[],"40236901e":18,"4027":4,"4030":4,"40340043":[],"4034668048":[],"4038":4,"4039":4,"404":[],"4045":4,"4048":4,"4055":4,"405890":11,"4059":4,"40599799":[],"4068":4,"4069":4,"4075":4,"4077":[],"408":2,"4082":[4,6],"4084":4,"4087":[],"4088":4,"4089":4,"409":2,"40927184e":6,"40a38ad763f1":6,"41":[2,4,16],"410":2,"4100":4,"4107":[],"411":2,"4115":4,"4117":4,"412":2,"4128":4,"4131":4,"41357064":[],"4143":4,"41433969":5,"4144":4,"4146":4,"41511965e":1,"4155":2,"4162706317":[],"4167":4,"4176":4,"4177":4,"418506":11,"4186":4,"41876267":5,"418763":5,"41882036e":6,"41882037e":6,"4192423635":[],"41958102e":18,"4198":4,"41990268":[],"41it":6,"42":[1,2,4,8,9,10,16],"420":[],"42037468e":18,"42078103":[],"421":2,"4212":4,"422":2,"4224":4,"423":2,"42340403e":18,"42394972":16,"424":2,"42441033":5,"42449643":[],"42450":[],"425":2,"4255":[],"4258989918":[],"426":[6,7],"42615374e":18,"42631342":[],"427017":11,"4275":4,"43":[1,2,4,7,16],"43054282":5,"43135183":16,"43294696e":18,"43330971e":6,"434932":11,"43493232":11,"435163":[],"43552433":16,"43579948e":6,"436462435":4,"43761347":[],"43766686":11,"4379":4,"438060758":[],"438136":[],"439230":6,"44":[1,2,4,16],"44089210e":5,"4410":[],"4411":[],"442600":11,"443217":[],"444":[],"44688507e":18,"44970586e":1,"45":[2,4,19],"450":[],"450257":11,"4504":[],"45290829":16,"45393214e":18,"4543859":[],"4557763":11,"455947":[],"456":[],"456418966187335":[],"457":[2,4],"457770268480242":[],"458027":[],"458078":11,"45960079":5,"46":[2,4,19],"4601":[],"461":[],"461175":16,"462":7,"46383925e":6,"464424":[],"4644244":[],"46508305":16,"46567887":16,"466":[],"46602982":[],"46696223":[],"469":[],"46932688":[],"4694":[],"46984697e":6,"469868":[],"46986815":[],"47":[4,19],"4703":4,"47042744":5,"470714":[],"47075725":6,"47116868e":6,"47125748":5,"47132891":5,"47387858":[],"47400238":[],"47432993":[],"4744":[],"47478057":[],"47485224":[],"475311":[],"47531107":[],"47610036":6,"47815203":11,"47920156":[],"47942814":5,"479465113":4,"48":4,"48089797":[],"48133064":[],"481979":6,"48212873":16,"48257387":19,"48316523e":18,"483257001":[],"4837":[],"48420165":[],"48476997":11,"48574149":16,"486873":[],"48687342":[],"489":[],"48994188":5,"49":[4,5,6,11,13],"490":[],"491":[],"49152":3,"492":[],"493":[],"49385454e":18,"4940954":0,"4959161509356135e":6,"495916150936645e":6,"495916150936654e":[],"495916150938325e":[],"49614357":18,"49636583":[],"497":[3,4],"498":[3,4],"499":[3,4],"4990":18,"4992":18,"4997":18,"49992743e":18,"4c4c7f":[9,10],"4d":3,"4f":6,"4y":8,"4y_i":10,"5":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],"50":[1,2,3,4,6,7,8,10,13],"500":[1,3,4,6,9,10,13],"500000":[],"50000455":5,"50000553":5,"50000718":5,"50000855":5,"50000969":5,"50001063":5,"50001142":5,"50001207":5,"50001261":5,"50001306":5,"50001343":5,"50001374":5,"500014":5,"50001414":5,"50001422":5,"50001439":5,"50001454":5,"50001466":5,"50001476":5,"50001485":5,"50001492":5,"50001498":5,"50001502":5,"50001506":5,"5000151":5,"50001512":5,"50001515":5,"50001517":5,"50001518":5,"50001519":5,"50001521":5,"50001522":5,"50001523":5,"50001524":5,"50001525":5,"501":[3,4],"50105159":[],"5018":18,"50227564e":6,"5031474174499113":18,"50314742":18,"50321091":5,"504167":[],"50416731":[],"506":0,"50680321e":18,"50727059":16,"50769230769231":[],"507d50":[9,10],"50846111e":6,"50846112e":6,"50it":6,"50j":13,"50x10":1,"51":[4,10],"510":1,"51004249":16,"51126895e":18,"512132":[],"51214899":6,"51265232":16,"51289697":11,"512897":11,"5138":[],"51389553":16,"514219":[],"51561271":16,"517350858882083":[],"5177783846":4,"518030":11,"51803019":11,"519842":[],"52":[3,4,13],"5222222222222223":1,"522758":[],"52307692307693":[],"5260627":[],"526744":11,"52723079":[],"52773051":[],"52799362":11,"528115":11,"52811546":11,"52874252":5,"529":[],"52911941":[],"52945798e":[],"53":[3,4,9],"5303329":11,"5305555555555556":1,"5312":[],"531280":[],"53189647":[],"53312754":16,"53423784":16,"53603432":[],"53611562":[],"53697476":[],"53703498":6,"5378811":11,"53794784":11,"537948":11,"53846153846155":[],"539261":11,"54":[3,4,6,18],"540":[],"54016188":11,"540162":11,"54039921":5,"54041041e":5,"541605":[],"54163136":13,"544439":[],"546166676":[],"54780216":[],"54it":[],"55":[1,3,4],"5501":[3,4,14],"550321":[],"5539":[],"55505907":16,"5555555555555556":1,"55578041":[],"5566":[],"557795":11,"55854694":11,"5594":6,"56":[1,3,4],"56033697":5,"561":[],"5611":[],"5615":[],"56198284":5,"5625":[],"56302854":16,"5639":[],"564":[],"564374":11,"565":[],"56536":0,"566":[],"56636616e":6,"567":[],"568":[],"569":1,"56912044e":6,"56939714":5,"56992937":[],"57":[0,4,8,19],"570":[],"5700":[],"571":5,"571105947979336e":6,"571105947979394e":6,"571105947979395e":[],"571105947979439e":[],"57154252":[],"57174058":[],"57201944e":6,"57266138":[],"57361898":[],"574465":11,"57673618":16,"57842073e":18,"57it":[],"58":[4,10,19],"583595":[],"58395707":[],"58428804":5,"584804":[],"5888888888888889":1,"589":[],"58986647":[],"59":[2,4],"590":[],"591":[],"591317992":4,"591594":5,"59159438":5,"592":[],"593":[],"5944444444444444":1,"59591979":[],"595920":[],"59592669":[],"59766":5,"597660":5,"59833875":13,"598392":[],"59839245":[],"59955801":13,"5cm":18,"5dd54edf2138":6,"5f":8,"5x":8,"5y":8,"6":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,16,17,18],"60":[1,2,3,4,6],"60000":4,"6019067271":4,"60236938":16,"60293962":5,"60420593":5,"60538875":16,"60619654":6,"6063219":[],"606439":[],"60673226":11,"60685864":6,"60791699":[],"6079169911277265":[],"60943791":16,"61":[2,4,7],"61033524":[],"6111111111111112":1,"612939":[],"613579":[],"61399851":11,"613999":11,"61463451":16,"614808":11,"61576236e":18,"618":[],"61808351":[],"618982":[],"6199381169260247":[],"62":[2,3,4],"62168613":[],"62364974":[],"62373464":11,"625":7,"626635268":[],"62767384":[],"62894215":5,"629961":[],"63":[0,1,2,3,4,6,7],"6300745149331701":[],"630224":[],"63025821e":6,"63207808":18,"63249532e":6,"63270833":[],"63395187":[],"63498144":5,"63675140":[],"6371293350711955":[],"63860687":16,"63901111":[],"63it":[],"64":[1,3,4,7,13,16],"64012627":5,"64113381e":18,"64141716e":18,"64293754":[],"64425009":[],"645":[],"64615384615385":[],"646283":11,"647":6,"64742912e":6,"647473":11,"649339":5,"64933923":5,"649382":11,"64x50":1,"65":[1,2,3,4,7,8,9],"65136857":[],"6530742540053943":[],"6562":[],"65626043":5,"65628853":16,"65885453":5,"659306":[],"66":[2,3,4],"66054752":[],"66087937":[],"66204648":6,"66226149":6,"66247212":[],"6628996975186953":[],"66292841":[],"66310422":[],"6638":[],"66410989":[],"66560":[],"66619972":11,"666200":11,"667":[],"668172":[],"668186":[],"66818635":[],"66it":6,"67":[2,4],"67047975e":6,"671089":[],"672721":[],"67314874e":5,"67588315":[],"67591616":[],"67697934":11,"67838309":[],"67882608":[],"68":[2,4],"68176047e":18,"68192193":5,"68246089":16,"68279358":16,"6834195":[],"68534263e":6,"68542204":5,"68545647e":[],"6869":[],"6887363571":4,"68929213e":6,"68944595":[],"689519":11,"69":[2,4,7,18],"690":[],"690569639355314":[],"69061276":11,"690617":[],"69069n_":18,"692":1,"69295955":[],"69484813e":5,"69493539":16,"69504801":6,"69634577e":6,"69695259":5,"69985355":[],"6999536":11,"69it":[],"6e75736fdab1":6,"6f7a6bd7d79f":6,"6m":[3,4],"6n_":18,"7":[0,1,2,3,4,5,6,7,8,9,11,12,13,16,18,20],"70":[1,2,4,6,7],"70127680":6,"70179437":[],"70205195":16,"70354373":[],"703716d317a7":8,"70434005":[],"7050":[],"70598996":[],"70653767":4,"70710678":5,"70769586":[],"70831425":[],"70832814":5,"70980493":16,"71":[1,2,4],"71131626":16,"7119":[],"712018":11,"7134":[],"71350226":[],"7151":[],"71721168":[],"718697":5,"71869727":5,"72":[2,4],"72174172":11,"722047011333792":5,"72271878e":6,"72347283":[],"7236674":5,"72373129":[],"724":3,"72522848":[],"72859758":5,"7293182":[],"72981762":8,"73":[4,6],"731000":[],"73174557":[],"73287103e":18,"733096":[],"73406033e":18,"73484667":[],"735738558766299":[],"73573856":[],"73752910":[],"73it":6,"74":[4,6,13],"740":[],"74081822":8,"74368436":16,"743u":[],"7448615806559786":[],"7469898175164704":[],"74840212":5,"749765":[],"75":[4,5,6,8,11],"7501749450963715":[],"750445":[],"750u":[],"75106135":16,"75118364":[],"751699":11,"75170092":5,"753846153846155":[],"75517445":[],"756352":[],"75838233":[],"75it":[],"76":[2,4,19],"76060096":[],"76066069":16,"763u":[],"7643536":13,"765":7,"76529528":[],"76674796":16,"76731400e":18,"76802186":[],"76923076923077":[],"76936315":5,"7694444444444445":1,"77":[2,4,13,19],"77034458":[],"770345":[],"77152076":5,"7718":9,"773329728649545":[],"77332973":[],"77333117e":18,"774300":[],"775":[],"77627886":13,"77632220e":18,"77636e":13,"77662945":[],"7767978193240488":11,"77714169":8,"77754132e":18,"7782028952":4,"77865169":[],"78":[2,4],"78011544":[],"78156479e":5,"78184120e":6,"7846153846154":[],"78941903":5,"78988962e":18,"79":[2,4],"79009329":16,"79093776":[],"79111643":5,"791123":[],"79145214e":18,"793167":[],"79394867":[],"7939646":[],"794282":11,"79449156":[],"79602861":[],"79754246":[],"797e":6,"79856831e":18,"7c394b1e8b71":9,"7d7d58":[9,10],"7f2b3a6174c2":13,"7m":4,"8":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,17,18],"80":[0,1,2,4,5,6,8],"800":[4,7],"80315282":5,"803153":5,"80354994":6,"80469739":5,"8055555555555556":1,"80561191":[],"80609615e":6,"80609616e":6,"8064":[],"80734875":[],"807349":[],"80738685":16,"80747253":16,"80842254":11,"808423":11,"80847477e":6,"81":[1,2,4],"81048318e":6,"8108619":[],"81160425":5,"81187794":16,"81333804":6,"81333805":[],"81388724e":18,"814":[7,11],"815563":[],"81633628":11,"816454":[],"81671821":[],"816847":[],"8175":[],"82":[2,4],"8219235992494145":[],"8219236":[],"82198978":5,"82650876":[],"826509":[],"8265786":5,"82747653":[],"827477":[],"82773778":[],"829648415811072":18,"82964842":18,"83":4,"8305555555555556":1,"83512277":5,"836874":5,"83687444":5,"83698677":16,"83999999999999":[],"84":4,"84062065":[],"84228957e":[],"84232163":16,"842436":[],"84292394":[],"842u":[],"84355903e":1,"84443254e":1,"84569271":13,"84780262":6,"84854738":[],"84923989e":6,"84924834":11,"84994524":5,"85":[1,4],"8503720991789538":5,"85091337":[],"85263220":6,"8527777777777777":[],"85278920e":5,"853241":[],"85324115":[],"853u":[],"85601992":5,"85758696":16,"858":[],"8583333333333333":1,"85953586":6,"85959007":[],"86":2,"8608479":[],"860848":[],"861":[],"86117291":5,"86134827":5,"86145244":11,"8638888888888889":1,"86478158":[],"86546962":[],"86623151":13,"86630":[],"8666666666666667":1,"86810":[],"869":0,"86905621":[],"86925797":5,"869258":5,"87":2,"870":0,"8702784034":4,"871":0,"872":0,"8722222222222222":1,"873":0,"87327414":[],"87381451":5,"875":1,"8759":13,"876":6,"87625493":[],"87647541":[],"8768":[],"878297":[],"87836018":[],"87940752":16,"87it":6,"88":[2,13],"88017651":11,"880177":11,"88031314":[],"88046261":5,"8805555555555555":1,"88118231":[],"88168312e":6,"881788":[],"883":[],"88336879":5,"884":[],"88477619":[],"885":[],"88512489":[],"88529063e":6,"8855417382991412":[],"88554174":[],"886":[],"88679947":[],"887":[],"8879":6,"88871662":16,"8888888888888888":1,"8894":6,"89":2,"8916666666666667":[],"89274639":16,"89288636":11,"893489":5,"89348922":5,"89410423":5,"8944444444444445":1,"89550839":16,"89565264":[],"8962476":[],"896248":[],"89873772":16,"89876748":[],"8991514":16,"89944994":5,"899450":5,"8f":6,"8g":6,"8n":16,"8x8":1,"9":[0,1,2,3,4,5,6,7,8,9,11,12,13,14,16,18],"90":[1,2,6],"900449":[],"90054363":[],"90075537":5,"9011":6,"90220243":5,"90266948":5,"90268858":[],"9027777777777778":1,"903824":[],"90382431":[],"9040":9,"90475506e":6,"90540529":11,"9055555555555556":1,"9083333333333333":[],"90884627":16,"90895045":16,"90940378":16,"90999452e":18,"91":19,"910":[],"9111111111111111":1,"91128596":5,"912u":[],"913":[3,4],"91379157":[],"913791573406831":[],"91383439":16,"91396388":[],"914":[3,4],"91416375":[],"91479093":[],"91492986e":6,"915":[3,4],"91549644":[],"916":[3,4],"9166666666666666":[],"917":[3,4],"91760278":5,"918":[],"91812702":5,"918992":[],"919":[],"92":[0,6,19],"92272314":0,"922u":[],"92343595":[],"924018":[],"92477093":[],"924e":6,"925":1,"92507116e":1,"9252772":11,"92578916":5,"92603747":16,"92626212":16,"92754397":[],"927544":[],"9277777777777778":1,"92819235":[],"92857143":7,"93":[],"9305555555555556":1,"930583":11,"931":0,"93155188":5,"93158979":5,"93248252":5,"932483":5,"932u":[],"933":5,"93414191":[],"93492130e":6,"93579127":16,"935u":[],"9361111111111111":[],"937":18,"937082":[],"93799826":5,"938":18,"9387":[],"939":[0,18],"94":[7,18],"9400":[],"941866404575299":[],"94226022e":6,"94284104":5,"94320205":5,"9444444444444444":1,"945":[3,4],"94548496":[],"94591015":16,"946":[3,4],"94639099":11,"94659383":[],"947":[3,4],"9472222222222222":1,"94735055":[],"947903":[],"94790323":[],"948":[3,4],"9482527":5,"949":[3,4],"95":[1,7,11],"95008046":6,"95079764":6,"95231424":5,"9527777777777777":1,"95284275":5,"953065564":[],"95351665":5,"954":18,"9549351910143222":[],"954u":[],"9555555555555556":1,"95569422":[],"955820c21e8b":4,"956563":11,"95684892":5,"95703":13,"95714723":[],"957147232685324":[],"95it":[],"96":[6,7,11],"960":18,"9601304850018328e":6,"960130485007504e":6,"960130485007934e":[],"9601304850213484e":[],"96024953":5,"96032148":16,"96084663":5,"961":18,"962":18,"9637117593816477":6,"9640435":5,"96489434":[],"9649652536":4,"96527903":16,"96551427e":18,"965548":[],"96606158":16,"96653373":[],"96686324e":18,"96688672":5,"9674916":5,"967809":11,"96863851":[],"96987657":[],"96992454":[],"97":7,"97005689":5,"97065296":[],"97108e":13,"9722222222222222":1,"97230501":[],"97243128":5,"97300836":5,"97497404e":6,"975":1,"97507735":5,"97547354":18,"97547354476579":18,"975510299261579":9,"9760832":[],"97705827":5,"977418":5,"97758848":5,"9777777777777777":1,"977880":[],"97788031":[],"9780387310732":20,"9780387848570":20,"9781492032632":20,"97898392":6,"97926491":5,"98":[0,1,7],"980":[],"98017611":13,"98036405":[],"9805555555555555":1,"98091621":5,"981321":[],"98139097":5,"98266587":16,"98275501":5,"983310":[],"98346748":[],"98399675":[],"98413059":5,"98454786":5,"985":18,"98566191":5,"986":18,"9861111111111112":1,"98680716":5,"98716878":5,"98756882":13,"98794823":16,"9879924":[],"98808176":5,"98822371":6,"9888888888888889":1,"989":18,"9890348":5,"9893149172528393":[],"9893447":5,"9898254753574576":[],"98982548":[],"9898ff":[9,10],"99":[6,7,11,13],"990":[],"99009525":5,"9902552771282336":[],"99049330":6,"99088801":5,"991":18,"99115119":5,"99176998":5,"992":18,"99242921":5,"99265097":5,"993":18,"99316252":5,"99353454":[],"993535":[],"99371056":5,"99389612":5,"993972":[],"99397245":[],"9943201":5,"99435648":[],"9947756":5,"99492986":5,"99519225":13,"99528218":5,"99539415":5,"9955282554647219":[],"99566069":5,"99578809":5,"996":5,"99608161":5,"9963961":5,"99650061":5,"9967458":5,"99700706":5,"99709215":5,"99724883":[],"99729756":5,"99751458":5,"99758326":5,"99775587":5,"99775949":[],"99793613":5,"99799099":5,"99813653":5,"99828624":5,"99829953":[],"9983295":5,"99845267":5,"998577":5,"99861053":5,"99871521":5,"99881845":5,"99884384":5,"99893323":5,"999":[9,18],"99901896":5,"99903755":5,"99911427":5,"99918546":5,"99919837":5,"99926459":5,"9993237":5,"99933188":5,"99938942":5,"9994385":5,"99944272":5,"99949306":5,"99953381":5,"99953475":5,"99957911":5,"99961294":5,"99965056":5,"99967865":5,"99970988":5,"9997332":5,"99975913":5,"99977416":[],"99977849":5,"99980002":5,"9998161":5,"99984732":5,"99987324":5,"99989476":5,"99991263":5,"99992746":5,"99993978":5,"99995":5,"9999555851685968":6,"999955585168597":6,"9999840939906267":[],"9999858320366368":[],"9x":6,"9y":6,"\u00f8yvind":[6,19],"abstract":1,"boolean":4,"break":[0,4,6,11,14],"byte":16,"case":[0,1,2,3,4,5,6,7,11,12,13,14,15,16,17],"catch":0,"char":[],"class":[0,1,3,4,6,7,8,9,11,12,13,18],"const":[],"default":[0,1,2,4,6,7,13,16],"do":[0,2,3,4,5,6,8,9,10,11,12,13,14,16],"ekstr\u00f8m":4,"eng\u00f8i":19,"export":9,"f\u00f8470":19,"final":[0,1,2,3,4,5,6,7,8,9,10,11,13,14,17,18,19],"float":[0,3,4,5,9,11,13,14,16],"function":[2,3,4,5,9,14,15,16],"import":[0,1,2,3,4,6,7,8,9,10,11,12,13,14,18],"int":[0,1,2,3,4,5,6,11,13,14,16,18],"long":[0,1,3,4,12,13],"m\u00f8svatn":6,"new":[0,1,2,3,5,6,7,8,9,10,11,13,14,16],"null":[],"public":[0,15],"return":[0,1,2,3,4,5,6,7,8,9,11,13,14,16,18],"s\u00f8rli":[],"s\u00f8rlie":19,"sch\u00f8yen":[6,19],"short":[4,5],"steinsv\u00e5g":[],"super":[3,5],"switch":0,"throw":[3,6,18],"true":[0,1,2,3,4,5,6,7,8,9,10,12,13,14,16,18],"try":[0,1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,18],"var":[5,6,10,11,18],"while":[0,1,3,4,5,6,7,8,9,11,12,13,18],A:[2,3,5,6,7,10,11,12,13,15,16,17,18,19,20],AND:2,And:[0,3,4,5,6,9,15,18],As:[0,1,2,3,4,5,6,8,10,12,13,16,18],At:[0,4,6,13],BE:0,Be:[2,15],Being:13,But:[0,1,2,3,5,6,9,10,18],By:[0,3,5,6,12,13,16],For:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20],IF:6,IN:20,If:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,18],In:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18,20],Is:11,Ising:[5,12],It:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],Its:[1,2,4,11],NO:[7,11],No:[3,4,5,6,7,8,9],Not:[0,1,5,6,17],OR:18,Of:18,On:[0,3,17,18,20],One:[0,1,3,4,5,6,7,8,11,12,13,18],Or:[0,1,6],Such:[6,12,18],That:[0,5,7,10,11,12,14,18],The:[4,10,13,14,16,17,18,19,20],Then:[0,1,5,6,8,9,10,11,12,13,14,16],There:[0,3,4,5,6,8,9,11,12,14,16,17,18,19],These:[0,3,4,5,8,9,10,11,12,13,14,16,18],To:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,16,18],With:[0,5,6,8,9,10,11,12,14,16,18],_0:[5,8,10,11,13],_1:[2,5,6,8,10,11,12,13,14,16],_2:[2,5,8,11,12,13,16],_3:16,_4:16,_9:13,_:[0,1,2,4,5,6,7,8,9,10,11,12,13,16],_________________________________________________________________:4,__call__:[3,4],__class__:10,__doc__:6,__future__:[8,9],__getitem__:[],__init__:[1,3,4,14],__main__:2,__mosek:5,__name__:[2,10],__traceback__:[3,4],_asarrai:[],_auto10:[6,12],_auto12:6,_auto1:[2,3,4,5,6,7,12,13,16,18],_auto2:[2,3,4,5,6,12,13,16,18],_auto3:[3,4,5,6,12,13,16],_auto4:[4,6,12,13,16],_auto5:[4,6,12,13,16],_auto6:[4,6,12,16],_auto7:[4,6,12,16],_auto8:[6,12],_auto9:[6,12],_ax:[],_base:8,_build:[15,20],_build_call_output:[3,4],_c:1,_call:[3,4],_call_flat:[3,4],_check_1d:[],_check_optimize_result:[7,11],_compon:11,_coordinate_desc:6,_copy_docstring_and_deprec:[],_decor:0,_depth:9,_fraction:9,_get_lin:[],_getitem_multilevel:[],_handl:[3,4],_i:[0,1,2,5,6,8,11,12,13],_inference_funct:[3,4],_interpolatefunctionerror:[3,4],_is_primit:[],_j:[0,1,2,3,5,6,8,13],_jit_compil:[3,4],_k:13,_l:12,_lambda:6,_leaf:9,_logist:[7,11],_m:10,_make_vjp:[2,13],_maybe_define_funct:[3,4],_multilayer_perceptron:1,_n:[2,5,8,11,13],_node:[2,9,13],_notokstatusexcept:[3,4],_np:[],_num_output:[3,4],_p:[5,8],_plot_arg:[],_process_traceback_fram:[3,4],_r:[3,4],_ratio:11,_sampl:9,_select_forward_and_backward_funct:[3,4],_split:[6,9],_src:[3,4,14],_stateful_fn:[3,4],_stateless_fn:[3,4],_t:13,_test:6,_trace:[2,13],_valu:[2,13],_varianc:11,_weight:9,a0:3,a0faa0:[9,10],a1:0,a2:0,a3:0,a4:0,a_0:0,a_1a:0,a_2a:0,a_3:0,a_3a:0,a_4:0,a_4a:0,a_:[0,1,16],a_h:1,a_i:[0,1,2,12],a_j:[1,12],a_k:[1,12],a_ndim:2,aaron:20,ab:[0,2,5,13,14],ab_channel:15,abandon:1,abbrevi:17,abid:18,abil:[0,10],abl:[1,4,5,6,7,10,12,13],abort:[],about:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,20],abov:[0,1,3,4,5,6,7,8,9,10,11,12,13,14,16,18],abovement:6,abscissa:13,absolut:[0,2,5,6,13],acceler:13,accept:[0,3,6,9],access:[0,3,4,11,18],accid:[4,6],accmod:5,accompani:0,accomplish:[8,9,13],accord:[0,1,2,5,6,9,12,13,14,18],accordingli:11,account:[0,3,5,13,18],accumul:[12,18],accur:[0,3,4,6,10,13],accuraci:[0,1,3,4,5,6,7,9,10,11,12],accuracy_scor:[0,1,10],accuracy_score_numpi:1,achiev:[0,1,5,6,8,12,16],aco:18,acquaint:15,acquir:[1,15],acr:0,across:[1,3,6,9,15],act:[1,3,16],action:18,activ:[0,2,3,4,9,17],actual:[0,1,4,5,6,8,11,16,18],ad:[0,1,3,4,5,8,13,16],ada_clf:10,adaboostclassifi:10,adadelta:13,adagrad:13,adam:[1,3,4],adapt:[4,6,13,20],add:[0,1,2,3,4,5,6,8,10,11,12,18],add_lin:[],add_outgrad:2,add_subplot:[1,7,12,14],addendum:5,addit:[0,2,3,5,6,7,8,9,10,12,13,15,16,18,19,20],addition:[12,13],address:[1,9,11,13,20],adjac:[3,12],adjoint:5,adjust:[0,5,12,13],admir:0,advanc:[4,6,12,20],advantag:[1,3,5,6,10,13,16],affect:3,affin:[0,3,8,11],afford:3,aforement:14,african:0,after:[0,1,2,4,5,6,9,11,12,13,15,16,18],afterward:0,ag:[0,7,17],ag_0:2,again:[0,1,4,5,6,7,8,10,11,12,13,18],against:[1,4,7,10],agegroup:7,agegroupmean:7,aggreg:[9,10],agorithm:10,agre:[5,6,18],ahead:9,ai:[0,20],aid:11,aim:[0,1,4,6,7,11,14,15,16],ainv:5,airplan:3,aka:5,al:[0,2,4,17,20],alarm:5,algebra:[0,3,5,13,15,17],algo:[],algorithm:[0,1,2,4,5,6,7,8,13,14,15,16,17,18,20],align:[0,2,5,6,7,8,13,18],all:[0,1,2,3,4,5,6,7,9,10,11,12,13,14,15,16,17,18,19,20],allevi:[1,13],alloc:[3,16],allow:[0,1,2,3,5,6,8,10,13,15,16],almost:[0,1,6,8,11,13,18],alon:[2,9],along:[2,3,4,5,6,9,10,11,15,16],alpha:[0,1,2,3,4,5,6,7,8,9,10,13,14,18],alpha_0:3,alpha_1:3,alpha_2:3,alpha_:10,alpha_i:[3,13],alpha_k:13,alpha_m:10,alpha_n:3,alpha_opt:13,alreadi:[2,3,4,5,6,10,12,15,16,18],also:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20],alter:1,altern:[0,1,4,5,6,7,8,9,11,13,16],although:[1,5,6,8,10,13],alwai:[0,3,5,6,12,13,18],am:4,ame2016:0,american:0,among:[0,3,5,9,10,12,16],amongst:5,amount:[0,1,3,4,6,8,10,14,15],an:[1,2,3,5,6,7,8,9,11,12,13,14,15,16,18,19,20],an_:18,anaconda3:[],anaconda:[0,1,15],analog:13,analys:6,analysi:[1,3,4,7,14,16,17,20],analyt:[0,2,3,5,6,7,12,13,15],analyz:[0,1,3,4,5,6,18],andrew:1,angl:[0,3,9],anharmon:3,ani:[0,1,2,3,4,5,6,7,8,9,10,12,14,18],anim:[4,12],ann:12,annot:[0,1,3,7,8],anoth:[0,1,3,4,5,6,7,8,10,11,12,13,16,18],anp:2,ans_vspac:2,ansatz:0,answer:[0,1,3,5,6,16],antialias:[2,6],anticip:4,anymor:[1,8],anyon:[4,8],anyth:[1,18],anytim:19,apach:1,apart:[11,13],api:[1,15],appar:2,appear:[0,1,3,13,16,18],append:[1,3,4,8,9,13],appendcon:5,appendvar:5,appli:[0,1,2,3,4,6,7,8,9,10,11,12,13,18,20],applic:[0,1,3,4,5,6,7,9,12,13,16,17,18,20],apply_gradi:4,approach:[1,2,4,5,6,9,10,11,12,13,15,18,20],appropri:[2,6,9,12,13,15,18],approx:[0,2,3,6,10,11,13,18],approxim:[0,1,2,3,4,5,6,7,10,11,13,18],apt:[0,15],aq:18,ar:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20],arang:[1,3,4,6,7,9,10,12],arbitrari:[1,4,6,8,12,13,18],arbitrarili:[0,1,11],arc:6,architectur:[3,4,12,20],area:[0,3,6,20],arg:[0,2,3,4,13],argc:[],argmax:[1,11],argmin:[4,10,14],argnum:[2,13],argnum_0:2,argnum_1:2,argsort:11,argu:[1,13],argument:[0,2,3,5,6,11,12,13],argv:[],argval:2,aris:[0,6,12,13,18],arithmet:[0,13,16],arm:[3,4,6,14],arma:[],armadillo:16,around:[0,1,4,5,6,11,18],arr:2,arrai:[0,1,2,3,4,5,6,7,8,9,11,12,13,14,15,18],arrang:3,arraybox:13,arriv:[0,6,9,11,16,18],arrow:12,arrowprop:8,art3d:13,art:[0,1,15],articl:[0,3,4,6,10],artifici:[0,2,7,12,20],artificialneuron:12,arug:13,arxiv:[3,4],as_fram:[],asarrai:[0,2,6,9],asc:5,ascii:[],ashrafi:19,ask:[5,6,11,12],aspect:[0,6,15],assembl:[0,3],assert:4,assess:[0,6],assici:4,assign:[0,7,8,9,12,13,14,17,20],associ:[0,6,9,12,14,18],assum:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],assumpt:[0,3,5,6,9,11,18],ast:[0,5,6],astyp:[4,9,10],asymmetri:0,asymptot:[4,6],async_wait:[3,4],atoi:[],atom:0,attempt:[0,4,6,7,8,10],attend:17,attent:[0,16],attr:[3,4],attract:[0,10],attribut:[0,9],attributeerror:[],audi:0,audio:[3,4],aurelien:[0,17,20],austfjel:6,author:[0,1,10,18],authour:0,auto:[6,9,10,18],autocor:18,autocorrelation_tim:18,autocorrelform:18,autocovari:18,autoencod:[4,15],autoencond:15,autograd:15,autom:[0,15],automac:16,automat:[0,1,2,3,4,11,15,16,17],automobil:3,autonom:[4,20],avail:[0,1,4,6,10,11,15,16,17,20],averag:[0,1,3,6,9,10,13,14,18,19],avg:[],avoid:[0,4,5,6,9,11,13,16],avx2:[],avx:[],awai:[2,3,6],awar:[2,10],award:19,ax:[0,1,2,3,4,6,7,8,9,10,11,12,13,14,16],axes3d:[2,6,13],axes_grid1:6,axessubplot:[],axhlin:8,axi:[0,1,2,3,4,6,7,8,9,10,11,12,13,14,16,18],axiom:5,axlabel:0,axvlin:[4,8],axvspan:4,b1:8,b2:8,b3:8,b:[0,1,2,3,4,5,6,8,9,10,12,13,14,18,19],b_1:[2,12,13],b_2:13,b_5:13,b_:[1,16],b_group:9,b_i:[0,1,2,12],b_ia_:0,b_index:9,b_is_vec:2,b_j:[1,12],b_k:[1,12,13],b_m:12,b_meta:2,b_score:9,b_valu:9,bachelor:17,back:[0,3,4,5,6,8,9,10,16,17,18],backbon:16,backend:[1,4],background:[17,20],backpropag:1,backtrack:9,backup:16,backward:[1,2,4,12,16],backward_pass:2,bad:6,badli:18,bag:[9,15,17],bag_clf:10,baggingboot:10,baggingclassifi:10,baggingtre:10,balanc:6,band:16,bandwidth:16,bar:[0,6,11],barber:20,bare:[4,10],base:[0,1,3,4,5,7,8,9,10,14,15,18,19,20],basi:[5,7,8,10,11,12,13,16],basic:[6,8,12,13,14,15,17,18],batch:[3,4,11,12,13],batch_shap:4,batch_siz:[1,3,4],batchnorm:4,bay:7,bayesian:[5,15,20],bc298b802fe2:6,becaus:[0,1,2,3,4,5,6,8,9,12,13,14],becom:[0,1,2,5,6,7,9,12,13,18],been:[0,1,2,3,4,5,6,11,12,13,15,16],befor:[0,1,2,3,4,5,6,7,8,12,13,14,16,18],beforehand:[0,18],begin:[0,1,2,3,4,5,6,7,8,9,11,12,13,14,16,18],behav:[1,6,13],behavior:[0,1,13],behaviour:12,behind:[0,1,6,8,13],behnoosh:19,being:[0,1,2,3,4,5,7,8,10,11,12,13,18],believ:[9,16],belong:[5,7,8,9,13,14],below:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,16,18],benchmark:10,bendik:[],benefici:[1,13],benefit:[0,1,4,11,13,15],bengio:[1,17,20],benign:[1,7],bennosh:19,besid:[4,5],bessel:5,best:[0,1,2,3,4,5,6,7,8,9,10,12,13,19],beta:[0,1,3,5,6,7,10,11,13],beta_0:[0,1,3,5,6,7,13],beta_0x_:0,beta_1:[0,1,3,5,6,7,10,13],beta_1x_0:0,beta_1x_1:[0,7],beta_1x_2:0,beta_1x_:0,beta_1x_i:[7,13],beta_2:[0,3,13],beta_2x_0:0,beta_2x_1:0,beta_2x_2:[0,7],beta_2x_:0,beta_3:3,beta_:[0,3,6,7,13],beta_i:[0,3,5],beta_j:[0,5,6,13],beta_k:13,beta_linreg:13,beta_m:10,beta_mg_m:10,beta_n:3,beta_p:7,beta_px_p:7,betavalu:5,better:[0,1,2,3,4,6,9,10,11,12,13],between:[0,1,2,3,4,5,6,7,8,9,11,12,13,14,18],beyond:[0,1,5,6,8,13],bf:[13,14,16,18],bgd:13,bia:[0,1,2,3,5,8,9,10,12,13,17],bias:[1,2,3,5,6,9,12],big:[0,1,2,5,6,14],bigger:[1,6],bigr:12,bike:9,bilek:19,billion:[3,12,15],bin:[0,7,18],binari:[0,3,5,7,9,10,12,17],binarycrossentropi:4,bind:0,binomi:[15,18],binsboot:6,bioinformat:0,biolog:[1,12,20],bios1100:15,bird:[0,3],bishop:[17,20],bit:[1,4,16,18],bitwis:18,bivari:2,bk:[0,13],bla:[5,16],black:[8,9,14],block:[6,10,15,16,18],blockingavg:[],blockingstd:[],blockingvar:[],blocksiz:[],blocksizemax:[],blocksizemin:[],blogpost:4,blue:[0,3],bmatrix:[0,1,3,5,7,8,11,13,16],bmi:1,bodi:[0,1,4,12],bold:1,boldfac:[0,5],boldsymbol:[0,1,2,3,5,6,7,8,10,11,13,14],boltzmann:[12,15],book:20,boost:[1,9,15,17],boostrap:10,bootavg:[],bootstd:[],bootstrap:[1,13,15,17],bootvar:[],bootvec:[],boston_dataset:0,bot:8,both:[0,1,4,5,6,8,9,10,13,14,15,16,18,19],bottl:7,bound:[0,5,8,12],boundari:[2,4,8,11,12],boundkei:5,box:[2,4,9],boxed_arg:2,boyd:[8,13],bracket:[4,18],brain:[1,7,12],branch:9,breast:[5,7,11],breviti:13,brew:[0,15],brg:8,briefli:0,bring:[0,5,6,10],broad:[0,3,4],brownle:4,brute:[3,5,11],bs:[8,9,10],buffer_s:4,bui:4,build:[0,4,5,6,10,13,16,18],built:[0,1,3,4,6],bunch:11,busi:0,bx:5,bzl:5,c1:[8,11],c2:[8,11],c95af3df0cdd:3,c:[0,1,2,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19,20],c_0:18,c_1:12,c_2:12,c_3:12,c_4:12,c_:[0,8,9,10,13,18],c_i:[12,13],c_k:18,ca:1,cabc613b8702:13,cach:10,cal:[0,8,10,12,13],calcul:[0,1,2,4,5,6,8,9,10,11,12,13,14,16,18],california:[],call:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,20],callabl:[3,4],callback:[3,4],calor:0,cambridg:[13,20],can:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,20],cancel:[0,13],cancellation_manag:[3,4],cancer:[5,10],cancerpd:7,candid:[8,9,10],cannot:[0,1,4,5,6,7,8,9,17,18],canopi:[0,15],cap:5,capabl:[0,1,8,13,15],capac:2,capita:0,captur:[4,11,12],captured_input:[3,4],car:[3,4],card:[0,7],cardin:1,care:11,carefulli:13,carlo:[0,6,15,18,20],carri:[2,6,7],cart:10,carvalho:19,casella:20,cast:1,cat:[3,4],categor:[0,1,3,9,11],categori:[0,1,3,7,10,12,14,17],categorical_crossentropi:[1,3],caus:[0,5,6,18],causal:0,causat:0,cax:1,cb:6,cbar:1,cbook:[],cc:[0,1,5,13],ccc:[5,12],cd_fast:6,cdf:18,cdot:[0,2,6,12,13,14,16,18],celebr:13,cell:4,center:[0,1,6,7,8,9,11,14,18],centr:20,central:[0,3,5,6,8,16],centroid:[14,18],centroid_differ:14,centuri:3,certain:[0,3,6,7,9,18],cg:13,cha:0,chain:[1,13,15,18],chanc:[1,5,13,18],chang:[0,1,2,3,4,5,6,8,9,11,12,13,14,16,18],channel:3,chapter:[0,6,10,11,16,17,20],charact:[0,3,5],character:[8,9,10,12,18],characterist:[0,1,3,10,13],charg:0,charl:0,chase:4,chd:7,chddata:7,cheap:5,cheaper:[1,13],check:[0,1,3,4,5,6,11,13,16],checkmark:3,checkpoint:4,checkpoint_dir:4,checkpoint_prefix:4,chemic:[],chen:10,chiaramont:2,choic:[0,1,2,3,4,6,9,12,13,14,16],choleski:[5,16],choos:[2,3,6,9,10,11,13,14],chosen:[0,1,2,6,8,9,10,13,18],chosen_datapoint:1,christian:20,christoph:[17,20],cifar10:3,cifar:3,cin:[],circ:[1,12],circl:[0,8,12],circuit:3,circumfer:9,circumv:[1,5,13],ckpt:4,clariti:18,class_nam:[3,9],class_val:9,class_valu:9,class_weight:[3,4],classic:[7,9,13],classif:[0,3,5,6,7,8,11,12,15,17,20],classifi:[0,1,4,7,9,10,11],classificaton:1,classifii:10,clean:1,clear:[1,5,10,12,13],clearli:[0,3,5,6,7,8,18],clever:[1,10],clf3:0,clf:[0,6,8,9,10],clf_lasso:6,clf_ridg:6,clip:[3,18],close:[0,1,2,4,6,8,9,11,12,13,14,18,20],closer:[3,5,13],closest:[8,11,13,14],closur:15,cloud:15,cluster:[0,1,4,6,11,15,17],cluster_label:14,cm:[1,2,3,6,8,13],cmap:[0,1,2,3,4,6,8,9,10],cmap_arg:6,cmath:[],cmb:17,cmd:9,cmu:[],cn_:18,cnn:[12,17],cnn_kera:3,cntk:15,co:[0,2,3,6,9,13],code:[3,4,6,7,8,13,15,16,17,18,20],coef0:8,coef:0,coef_:[0,5,6,8,9,13],coeff:5,coeffici:[0,3,5,6,7,8,9,13,16],coerc:[0,6],coin:[10,18],coin_toss:10,col:[0,11],colab:15,cold:9,colinear:0,collaps:8,collect:[0,2,6,10,11,15,18,20],collinear:5,color:[0,3,4,6,8,9,10,18],color_channel:3,color_cod:6,colorbar:[1,6],colsample_bytre:10,colsaobject:10,colspec:0,column:[0,1,2,5,6,7,8,9,11,12,16],columntransform:9,com:[3,4,6,14,15,20],combin:[1,2,5,6,7,10,18],come:[0,1,3,4,5,12,13,14,17],command:[0,1],comment:[0,4,5,6],commerci:[0,15],commod:0,common:[0,1,3,5,6,7,9,11,13,14,18],commonli:[0,1,4,6,7,9,13,14],commun:[0,12],commut:3,commutatitav:3,compact:[0,1,3,5,6,7,9,11,12,13,14],compar:[0,3,4,5,6,11,13,16],comparison:[2,4,13],compat:7,compet:0,competit:10,compil:[0,1,3,4,15,16],complet:[0,2,3,4,9,12],completenn:12,complex:[1,5,8,9,11,12,13],complic:[0,1,9,13],compon:[0,1,3,4,5,6,7,9,14,15,17],components_:11,compos:[9,12,14],compphys:[6,15,17,20],compress:0,compris:6,compromis:5,compulsori:15,comput:[0,1,2,3,4,5,6,7,8,10,11,12,13,15,16,17,18,20],computation:[0,3,6,9,13,18],con:5,concaten:[2,4,6,14],concav:[1,13],concentr:[0,10],concept:[0,2,15],conceptu:[12,13],concern:[0,1,4,7],conclud:[0,5,13],conclus:1,cond:2,conda:[0,1,15],condit:[0,2,4,5,6,8,9,11,13,18],conduct:15,condwav:2,coneqp:5,confid:[0,5,6,7,8],configur:3,confirm:[5,12],confus:[5,6,10,16],confusion_matrix:9,congruenti:18,conjug:[4,8],conjugaci:13,conjunct:3,connect:[0,1,3,4,9,11,12,13,16],consequ:[5,6,8,10,12,13],conserv:[5,14],consid:[0,1,2,3,5,6,7,8,9,10,12,13,16,18],consider:[0,1,5,13],consist:[0,1,2,3,4,6,12,13,18],constant:[0,2,4,5,6,8,12,13,18],constitu:0,constitut:[2,6],constrain:[1,3,5,7,11],constraint:[5,6,8,13],construct:[0,1,2,3,5,6,7,8,9,10,11,16,18,20],contact:0,contain:[0,2,3,4,5,6,7,8,9,11,12,13,16,18,20],contemporari:20,content:[1,15,16],context:[3,4,6,10,13],contigu:16,continu:[0,1,2,3,4,5,6,7,8,9,10,12,13,16,18],contour:[9,10,13],contourf:[8,9,10],contrast:[1,4,9,10,12],contribut:[0,3,5,13,18],contributor:0,control:[0,1,3,9,13,15],conv2d:[3,4],conv2dtranspos:4,conv:[3,4],conveni:[0,5,6,12,13,16],convent:12,converg:[1,2,4,5,6,7,8,11,13,14],convergencewarn:[1,6,7,8,11],convert:[0,1,4,5,9,11,13,16],converttomatrix:4,convex:[4,5,7],convinc:13,convolut:[1,4,15,17],cool:[4,9],coolwarm:6,coordin:[5,12,14],coorel:0,copi:[0,1,14],core:[2,3,4,10,13],corel:0,coronari:7,corr:[0,5,7,11],correalt:[11,15],correct:[0,1,2,3,4,5,13,16,18],correctli:[1,2,6,10],correl:[0,1,3,5,6,7,10,12,13,15,18],correlation_matrix:[0,5,7,11],correspond:[0,3,5,6,8,9,11,12,15,16,18],cortex:12,cosin:[3,6],cost:[0,2,3,5,6,7,8,9,12,13],cost_deep_grad:2,cost_funct:2,cost_function_deep:2,cost_function_deep_grad:2,cost_function_grad:2,cost_grad:2,cost_sum:2,costol:13,could:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,16,18],coulomb:0,count:[0,9,17,18,19],countor:13,coupl:[4,5,6],cours:[0,1,3,5,11,17],courvil:[17,20],cout:[],cov:[5,6,11,16,18],cov_xi:[5,11],cov_xx:[5,11],cov_yi:[5,11],covari:[0,7,15,16],covariance_matrix:[5,11,14],cover:[0,5,15,17,20],covert:0,covxi:18,covxx:18,covxz:18,covyi:18,covyz:18,covzz:18,cpu:1,cpu_feature_guard:[],craft:3,creat:[1,2,3,4,5,6,9,10,11,12,13,15],create_biases_and_weight:1,create_convolutional_neural_network_kera:3,create_neural_network_kera:1,create_x:[5,11],credit:[0,7],crim:0,crime:0,criteria:[0,4,9,10,14,18],criterion:[9,10,13],critic:6,cross:[0,1,3,7,9,10,13,15,17,18],cross_entropi:4,cross_val_scor:6,cross_valid:[7,10],crossvalid:6,crucial:[1,18],cs231:3,cs:17,csr_matrix:16,cstdlib:[],csv:[0,4,6,7,9],ctnk:1,ctx:[3,4],cubic:0,cumbersom:5,cumsum:[10,11],cumul:[10,18],cumulative_heads_ratio:10,cup:5,current:[1,2,3,4,6,13,14],curs:0,curv:[6,7,10,12],curvatur:13,custom:[5,6,14],custom_cmap2:[9,10],custom_cmap:[9,10],cutpoint:9,cv:[6,7,10],cvxbook:13,cvxopt:[5,8],cyber:20,cycl:[1,12],d1:5,d2:5,d2_g_t:2,d3:5,d670a873ab0c:5,d985fb40c43d:6,d:[1,2,3,4,5,6,7,8,9,10,11,13,14,16,18,19],d_f:13,d_g_t:2,d_net_out:2,da:3,dagger:[5,16],dai:[1,9,15],dalen:[],damp:3,darget:9,darkr:18,dat:0,dat_id:[0,6,7,9],data1:14,data2:14,data3:14,data4:14,data:[2,4,5,8,10,12,13,14,16,20],data_handl:[3,4],data_id:[0,6,7,9],data_indic:1,data_modul:[],data_path:[0,6,7,9],data_url:[],databas:1,datafil:[0,6,7,9],datafram:[0,4,5,7,9,11],datapoint:[1,5,6,7,11,13],dataset:[0,4,6,7,8,9,10,11,13,14],datatyp:4,date:0,daughter:10,david:20,dbh:1,dbo:1,dcomposit:16,ddot:2,dead:1,deadlin:17,deal:[0,1,3,5,6,8,11,13,14,16,18],debt:7,debug:[5,6],decad:[0,3],decai:[0,13,18],decemb:17,decent:10,decid:[0,2,3,5,6,9],decim:0,decis:[0,1,8,11,15,17,20],decision_funct:8,decision_tre:9,decisiontreeclassifi:[9,10],decisiontreeregressor:[0,9,10],declar:[0,4,16],decompos:[5,6,16],decomposit:[0,6,12,17],decompost:5,deconvolut:3,decorrel:[10,13],decreas:[1,2,4,5,6,10,11,13],deduc:0,deep:[3,7,12,13,15,17,20],deep_neural_network:2,deep_param:2,deep_tree_clf1:9,deep_tree_clf2:9,deep_tree_clf:[9,10],deepen:[5,15],deeper:[0,3,4],deeplearningbook:20,deer:3,def:[0,1,2,3,4,5,6,7,8,9,10,11,13,14,18],def_covari:18,def_funct:[3,4],default_tim:4,defect:5,defici:5,defin:[0,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],definit:[1,2,5,6,8,10,11,12,13,16,18],defint:18,defun:[3,4],defvjp:2,degre:[3,5,6,8,9,10,11,18],del:1,delet:6,delimit:4,deliv:17,delta:[0,2,3,6,8,12,13,14],delta_0:3,delta_1:3,delta_2:3,delta_3:3,delta_4:3,delta_5:3,delta_:[1,16],delta_h:[0,1],delta_j:[3,12],delta_k:12,delta_l:[1,3],delta_n:[0,3],delug:15,delv:0,demand:13,demonstr:[0,3,5,6,7,11,12,15],den:4,denomin:[1,5],denot:[1,2,6,7,13,18],dens:[1,3,4],dense_1:4,densiti:[0,2,6,18],depart:19,depend:[0,1,2,4,5,6,7,8,11,12,13,15,16,18],depict:18,deploy:[0,15],deprec:[2,3,6,13],depth:[0,3,9,10,16],deriv:[0,1,2,6,7,8,10,11,13,15],derivati:13,descend:[5,9,11],descent:[0,1,3,7,8,12,17],descr:[],describ:[0,2,4,5,6,8,10,11,12,13,16],descript:[0,8,9],design:[0,1,3,4,5,6,7,10,11,12,13],designmatrix:0,desir:[0,2,4,5,13,14],despit:[1,12],destroi:16,det:[5,16],detail:[0,6,11,13,14,16],detect:[3,8,12],determin:[0,2,3,4,5,6,8,9,10,11,12,13,16,18],determinist:[7,13,18],dev:1,develop:[0,3,5,8,10,11,12,15,16,17],deviat:[0,1,2,4,5,6,18],device_nam:[3,4],devis:12,df:[4,8,11,13],di:[0,5],diag:[5,8],diagnost:[1,10],diagon:[0,5,7,13,16,18],diagonaliz:5,diagram:10,diagsvd:6,dice:[6,18],dict:[6,8],dict_kei:[],dictionari:0,did:[0,1,5,6,7,10,11,14],die:1,diff1:2,diff2:2,diff:2,diff_ag:2,diffeent:8,differ:[0,1,2,3,4,5,6,9,10,11,12,13,14,15,16,18,20],differenti:[3,15,16,17],differential_oper:[2,13],difficult:[0,1,6,10,13,18],difficulti:[0,1,13],diffonedim:2,digit:[0,1,3,4,6,17,19],dilemma:13,dilut:1,dim:[4,11,14,16],dimens:[0,1,2,3,4,5,8,11,14,16],dimension:[0,4,5,6,9,11,13,14,15,16],dimensionless:[0,3],diment:16,dimnsion:4,diod:3,direct:[0,1,2,4,11,12,13,14],directli:[1,4,5,6,18],directori:[],disabl:[3,4],disadvantag:0,disappear:[3,6],disc_loss:4,disc_tap:4,discard:[6,11],disciplin:[0,3,12],disclaim:18,discourag:13,discov:0,discover:5,discret:[1,3,5,7,13],discrimin:[4,7,10,11],discriminator_loss:4,discriminator_loss_list:4,discriminator_model:4,discriminator_optim:4,discuss:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20],diseas:7,disguis:6,disord:[1,7],displai:[0,1,3,4,5,6,7,8,9,10,11,12,14,18],displaystyl:[0,5],displot:[],disregard:0,dissimilar:[11,14],dist:14,distanc:[0,8,9,11,14,18],distance_list:9,distinct:[3,7,8,9,10,14],distinctli:8,distinguish:[0,4,7,8,18],distplot:0,distribut:[0,1,4,6,7,10,11,13,14,15,16],distrubut:[0,15],div:5,dive:[0,8,16],diverg:[1,13],divid:[0,1,3,5,6,8,9,11,12,18],divis:[6,8,9,13,16,18],dna:7,dnn1:4,dnn2_gru2:4,dnn:[0,1,2,4,12],dnn_kera:1,dnn_model:1,dnn_numpi:1,dnn_scikit:[0,1],doc:[6,15,17,20],document:[4,7,11,13],doe:[0,1,2,3,4,5,6,8,10,11,12,13,16,18],doesn:[3,9,12],dog:[1,3,4],domain:[5,8,13],domin:0,don:[0,1,3,5,6,8,11,13,15],done:[0,2,3,4,5,6,9,10,11,13,16],dot:[0,2,3,5,6,7,8,9,10,11,12,13,16,18],doubl:[3,4,16],doubli:1,down:[0,3,6,9,11,12,13],download:[0,1,3,5,6,16,20],downsampl:3,dozen:1,dq:6,drag:13,dramat:11,drastic:4,draw:[4,6,10,13],drawback:[0,1,3,13],drawn:[1,4,6,7,11,18],drive:[3,4],driven:3,drop:[0,1,5,6,11,13,18],dropna:[0,6],dropout:4,ds:5,dt:[2,3,13,18],dtype:[0,1,2,3,4,14,16],dualiti:6,dub:0,due:[1,2,5,6,8,10,12,13],dummi:0,dure:[0,1,3,4,8,9,11,15],dwell:0,dwh:1,dwo:1,dx:[2,3,8,18],dx_1:18,dx_1p:6,dx_2p:6,dx_mp:6,dx_n:18,dxp:6,dy:[1,8,18],dynam:4,dysth:19,dz:8,e:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,18,19],e_:[0,2],each:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19],eager:[3,4],eapprox:0,earli:[1,3,4,13],earlier:[0,5,7,8,9,11,12,13],earthexplor:6,eas:[6,9,14],easi:[0,5,6,7,8,9,10,11,12,13,15,16],easier:[5,6,8,9,13,18],easiest:13,easili:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,16],eastern:19,ebind:0,eblock:9,economi:5,ecosystem:15,ect:17,edg:3,edgecolor:6,edu:13,educ:0,eface79dac2c:10,eff:18,effect:[1,4,10,13,18],effic:1,effici:[0,3,10,13,15,16,18],efron:6,egrad:13,eig:[5,11,13,16,18],eigen:18,eigenpair:[5,11],eigenvalu:[0,5,8,11,13,16],eigenvector:[5,11,13],eight:16,eigval:[16,18],eigvalu:[11,13],eigvec:[16,18],eigvector:[11,13],eispack:16,either:[1,5,6,7,8,9,10,11,13,18],ekstrom:[],elabor:18,elarn:3,electr:[0,3,12],electur:20,eleg:11,element:[1,2,3,4,5,6,7,8,11,12,13,15,16,17,20],elementari:[10,13,16],elementwis:[3,13],elementwise_grad:[2,13],elif:[2,3,4,14],elim:16,elimin:[3,5,8],els:[1,2,3,4,7,9,12,13,16],elu:1,elus:0,email:[17,19],embed:[0,11],embodi:6,emit:18,emner:[17,20],emphas:[0,10,15],emphasi:[0,15,20],empir:[1,11,18],emploi:[0,1,5,6,11,13,18],employ:0,empti:[6,10],emul:12,en:[15,20],enabl:11,enbodi:6,encod:[0,3,5,9,11,14],encompass:[0,18],encount:[0,1,5,6,7,13,18],end:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],end_box:[2,13],end_nod:[2,13],end_valu:[2,13],endl:[],endpoint:[3,6],energi:[0,4,6],enet_coordinate_desc:6,enforc:12,eng:20,engin:[0,1,3,4,15],english:20,enorm:3,enough:[0,6,13],ensembl:[1,9,17],ensur:[0,1,2,3,5,6,11,13,18],ensure_initi:[3,4],enter:[5,6],enthought:[0,15],entir:[1,3,7,9,15,18],entiti:[9,12,16],entri:[0,5,8,11,12,16],entropi:[1,3,7,10,13],enumer:[0,1,2,3,4,6,8],env:[0,1,2,3,4,5,6,7,8,11,13,14,18],environ:[2,15,20],eo:[0,6],eol:0,eosfit:0,epoch:[0,1,3,4,12,13],epsilon:[0,5,6,7,13],epsilon_0:0,epsilon_1:0,epsilon_2:0,epsilon_:0,epsilon_i:0,eq:[3,13,14,16,18],eqnarrai:[3,5,6],equal:[0,1,2,3,4,5,6,8,9,11,12,13,14,16,18],equat:[1,3,4,5,6,7,8,9,10,11,13,14,16,17,18],equilibrium:[2,12],equiv:[3,13,16,18],equival:[0,1,5,8,11,13,15,16],erf:18,err:[0,10],err_:6,err_sqr:2,errat:13,errno:[],erron:2,error:[1,2,4,5,6,7,9,11,12,13,15,16,18],error_estimate_corr_tim:18,error_handl:[3,4],error_hidden:1,error_output:1,escap:13,especi:[1,3,9,12,13],essenti:[0,5,6,9,10,12,14,17,18],establish:[0,6,10,11],estim:[0,1,5,6,7,10,11,13,15,18],estimated_mse_fold:6,estimated_mse_kfold:6,estimated_mse_sklearn:6,et:[0,2,4,17,20],eta0:[8,13],eta:[0,1,3,8,12,13],eta_:13,eta_t:13,eta_v:[0,1,3],etc:[0,1,3,5,7,8,9,11,12,13,14,15,16,18],ethic:15,etsim:6,euclidean:[0,14],evalu:[0,2,3,4,5,6,9,13,18],evalut:13,even:[0,1,3,4,5,6,8,9,10,11,12,13,14,15,16,18],evenli:4,event:[3,4,5,7,10,14,18],eventu:[5,6,11,12,13,19],everi:[0,1,2,3,4,5,6,9,10,11,12,13,14,15,18],everyth:[4,12],everywher:[4,13],evolv:0,exact:[0,2,5,11,12,13,16,18],exactli:[0,3,4,6,12,15],examin:6,exampl:[5,11,12,13,15,16,17,18,20],exce:[1,12,13],excel:[0,1,4,5,10,20],except:[3,4,5,6,8,9,16],excess:0,excit:0,exclud:[1,6,12],exclus:[0,1,3,6,18],execut:[2,3,4,5,13],executing_eagerli:[3,4],exemplifi:13,exercis:[5,15,17],exhaust:6,exhibit:[0,5,6,8],exist:[0,1,2,3,5,6,7,8,9,13,16,20],exit:[5,16],exp:[0,1,2,5,6,7,8,10,11,12,13,18],exp_term:1,expand:[5,7,11,13],expans:[0,3,5,8,10,12,13],expect:[0,1,5,6,7,11,12,13,15],expectation_value_of_h_wrt_p:18,expens:[6,10,13],experi:[0,1,6,8,13,15],experiment:[0,3,4,6,9,14,18],experimental_get_tracing_count:[3,4],expert:[1,9],explain:[0,6,9,10,11,13],explained_variance_ratio_:11,explanatori:0,explicit:[0,3,6,13,16],explicitli:[0,4],explod:1,exploit:[0,3,12,13],explor:[1,4,6,8,13,15],expon:1,exponenti:[0,1,5,6,10,13,18],export_graphviz:9,export_text:9,exporttext:9,expos:15,express:[0,2,3,5,6,7,10,12,13,16,18],exptmean:18,exptvari:18,extend:[2,5,7,11,13,15],extens:[0,12,15],extent:[0,1,6,20],extern:[3,6,9],extra:[1,3,5],extract:[0,3,5,6,7,8,11,13,16],extrapol:0,extrem:[0,1,4,5,6,7,8,9,13,16],extremum:13,extrins:11,ey:[0,5,6,13,14,16],f11:0,f12:0,f13:0,f1:13,f1_grad:13,f1d:13,f2:13,f2_grad_x1:13,f2_grad_x1_analyt:13,f2_grad_x2:13,f2_grad_x2_analyt:13,f3:13,f3_grad:13,f3_grad_analyt:13,f4:13,f4_grad:13,f4_grad_analyt:13,f5:13,f5_grad:13,f6:13,f6_for:13,f6_for_grad:13,f6_grad_analyt:13,f6_while:13,f6_while_grad:13,f6d7a289d493:[],f7:13,f7_grad:13,f7_grad_analyt:13,f8:13,f8_grad:13,f9:[0,13],f9_altern:13,f9_alternative_grad:13,f9_grad:13,f:[0,1,2,3,4,5,6,7,8,10,12,13,14,16,18,19],f_0:[3,10],f_1:[10,13],f_2:[12,13],f_3:12,f_:10,f_d:18,f_grad:13,f_grad_analyt:13,f_i:[6,12],f_m:[3,10],f_n:3,f_raw:2,f_vec:2,f_wrap:2,face:13,facecolor:[6,8,18],facil:[0,15],facilit:12,fact:[0,1,3,5,9,11,12,13],factor:[0,1,3,5,6,9,10,11,13,16,18],factori:13,fade:6,fafab0:[9,10],fail:[0,6,7,8,11,13,19],failur:7,fairli:[1,2,18],fake:4,fake_loss:4,fake_output:4,fall:[8,9,17],fals:[0,1,2,3,4,5,6,7,9,10,13,14,16],famili:[0,7,8,18],familiar:[0,3,5,6,8,15,16,18],famou:[6,12],far:[0,3,4,5,6,8,11,12,13,14],fashion:[0,9,10],fast:[1,3,6,10,12,13,15,18],faster:[1,11,13],fastest:[13,16],favor:7,favorit:18,fc:3,fdf8a5d7c717:2,fdfcc778e1f8:4,fe5b9d300cc0:6,featur:[0,1,3,5,6,7,8,10,11,12,13,15,18],feature_nam:[0,1,7,9],feautur:9,fed:1,feed:[0,2,3,11,15,17],feed_forward:1,feed_forward_out:1,feed_forward_train:1,feedback:4,feeddorward:4,feedforward:[1,4,12],feel:[0,5,6,11,13,15,19],feet:0,fetch:6,fetch_california_h:[],fetch_openml:[],few:[1,3,4,5,9,18],fewer:[0,9,11],ffnn:[1,12],field:[0,3,6,12,15],fifth:[0,6],fig:[0,1,2,3,4,6,7,12,13,14],fig_id:[0,6,7,9],figaxi:18,figsiz:[0,1,2,3,4,6,7,8,9,10],figur:[0,1,2,3,4,5,6,7,8,9,10,12,13,14,15],figure_id:[0,6,7,9],figurefil:[0,6,7,9],file:[0,1,2,4,5,6,7,9],file_prefix:4,filenam:[],filenotfounderror:[],fileout:[],filepath_or_buff:0,fill:[5,9],filter:[3,4],filtered_flat_arg:[3,4],filtered_tb:[3,4],financ:0,find:[0,1,2,3,5,6,7,8,9,10,11,12,13,14,15,18],find_top_boxed_arg:2,fine:[0,14],finish:2,finit:[3,5,6,12,13,18],first:[0,1,2,3,5,6,7,8,9,10,11,13,14,16,17,18,20],firsteigvector:11,fit:[1,3,4,5,6,7,8,9,11,12,13,18],fit_beta:6,fit_intercept:[0,5,6],fit_mod:9,fit_transform:[0,6,8,9,11],fiti:0,five:[0,9],fix:[0,3,4,6,10,11,12,13],fixedformatt:6,fixedloc:6,fkkt:5,flag:4,flat:[12,13],flatten:[1,3,4,5,16],flexibl:[1,6,8,10,12],float32:[4,9],float64:[4,16],flop:[5,16],flow:[1,4,12],fluctuat:5,fly:11,flyvbjerg:[],fm:0,fma:[],fmax:3,fmesh:13,fn:[3,4],focu:[0,3,4,5,6,15,20],focus:[1,6,7,16],fold:[6,9],folder:[0,4,6],follow:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20],font:[0,7,18],fontdict:18,fontsiz:[1,6,8,9,10,18],fontweight:1,footprint:3,foral:8,forc:[0,5,6,10,11],forcast:4,forecast:[4,12],forelesningsvideo:17,forest:[0,1,9,15,17],forget:11,form:[0,3,4,5,6,7,8,9,11,12,13,15,16,18],formal:[3,4,14,18],format:[0,1,2,3,4,6,7,8,9,10,11,15,18,20],format_data:4,formatstrformatt:[6,13],formul:[4,6,11,14],formula:[3,13,18],forth:[4,12],fortran2003:15,fortran90:18,fortran:[0,15,16],fortun:[0,11],forward:[0,3,6,15,16,17],forward_backward:[3,4],found:[1,2,4,5,6,12,13],foundat:15,four:[4,5,6,8,12,16,17],fourier:0,fourierdef1:3,fourierdef2:3,fourierseriessign:3,fourth:12,fr:5,frac:[0,1,2,3,5,6,7,8,9,10,11,12,13,14,16,18],fractal:[],fraction:9,frame:7,framework:[1,8,10,18],frank:[5,11],frankefunct:[5,6,11],free:[0,6,11,13,15,16,18,19,20],freecodecamp:15,freedom:5,freeli:0,frequenc:[3,6,7,18],frequent:[0,8,9,13],frequentist:15,fresh:10,frida:19,fridai:17,friedman:[6,17,20],friendli:4,frog:3,from:[0,1,2,3,4,6,7,8,9,11,13,14,15,16,17,18,19,20],from_cod:9,from_logit:[3,4],from_tensor_slic:4,fromnumer:2,front:[0,4,5],fstream:[],fulfil:[2,5,12],full:[0,1,3,5,7,9,10,13,18],full_matric:5,fulli:[3,6,12,17,18],fun:[2,13,15],fun_nam:[],func:[0,2],functionali:11,fundament:[0,6,15],funtion:2,further:[2,9],furthermor:[0,3,5,6,7,11,12,13,15],futur:[0,4,8,9],futurewarn:[],fx:5,fy:[17,19],g0:2,g42mrgv128v34gnnhxwk9nrc0000gp:[],g:[0,1,2,3,4,5,6,8,9,10,11,13,18],g_0:2,g_1:[2,10],g_2:[2,10],g_:[2,9,10],g_analyt:2,g_dnn_ag:2,g_euler:2,g_i:2,g_m:[3,10],g_n:3,g_re:2,g_t:2,g_t_d2t:2,g_t_d2x:2,g_t_dt:2,g_t_hessian:2,g_t_hessian_func:2,g_t_jacobian:2,g_t_jacobian_func:2,g_trial:2,g_trial_deep:2,g_vec:2,gain:[1,5,9,10,13],galleri:0,game:4,gamma1:8,gamma2:8,gamma:[0,2,8,9,10,11,13],gamma_0:10,gamma_1:10,gamma_1x:10,gamma_:0,gamma_i:[0,8,18],gamma_j:13,gamma_k:13,gamma_m:10,gamma_x:0,gap:[6,8],gate:[4,12],gather:[0,1,12],gaug:12,gaussbacksub:16,gaussian:[4,5,6,8,14,18],gaussian_point:14,gaussian_rbf:8,gave:13,gbc:17,gca:[2,6,8,13],gd:1,gd_clf:10,gdclassiffiercgain:10,gdclassiffierconfus:10,gdclassiffierroc:10,gdm:13,gdregress:10,ge:[1,5,7,18],gemv:5,gen:[],gen_loss:4,gen_tap:4,gender:0,genener:4,gener:[0,1,2,3,5,6,8,10,11,12,13,14,16,18,20],generallay:12,generate_and_save_imag:4,generate_imag:4,generate_latent_point:4,generate_simple_clustering_dataset:14,generated_imag:4,generator_loss:4,generator_loss_list:4,generator_model:4,generator_optim:4,genexpr:[],genom:15,geodes:11,geometr:[0,13],geometri:5,georg:20,geotif:6,geq:[2,5,8,9,13],geron:[0,17,20],get:[0,1,2,3,4,5,6,7,9,10,11,13,15,16,18],get_dummi:9,get_loc:[],get_next_color:[],get_paramet:2,get_split:9,get_yaxi:8,get_yticklabel:6,getsolutionslic:5,getval:[],gg:[],gibb:15,gif:4,gini:10,gini_index:9,git:[0,15],github:[0,3,4,6,14,15,17,20],gitlab:[0,15],give:[0,1,2,3,5,6,7,8,9,10,12,13,14,15,17,18,20],given:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],global:[6,7,13],glorot:1,gmail:[],go:[0,1,3,5,6,8,9,11,12,13],goal:[0,7,9],goe:[0,1,2,5,6,13,14,16],golden:13,gone:5,gong:1,good:[1,3,4,5,6,9,10,11,13,15,17,18,20],goodfellow:[4,17,20],googl:[1,3,4,14,15],got:[1,6],gov:6,gp:20,gpu:1,grad:[2,13],grad_analyt:13,grade:17,gradient:[0,3,4,7,8,9,12,15,17],gradient_desc:3,gradientboostingclassifi:10,gradientboostingregressor:10,gradients_of_discrimin:4,gradients_of_gener:4,gradienttap:4,gradual:[1,14],grai:[4,6],graph:[1,9,11,12,13],graph_from_dot_data:9,graph_funct:[3,4],graphic:[0,1,9],grasp:0,gray_r:[1,3],grayscal:3,great:[5,13],greater:[1,7,18],greatli:13,greedi:9,green:[0,3,9,18],grei:4,grid:[1,3,6,7,8,12,18],grossli:13,ground:0,group:[0,6,7,9,14,15,17],groupbi:0,grow:[1,3,9,10],growth:0,gru:4,guarante:[0,3,4,13,18],guess:[1,4,10,13,14],guestrin:10,guid:1,guilherm:19,h1:2,h21:17,h:[0,1,5,6,8,13,18,20],h_1:[2,13],h_2:[2,13],h_:[0,13],h_m:10,ha:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],habit:0,had:[0,1,6,7,13],hadamard:[1,12],half:[1,8,9],halv:10,hand:[0,1,2,3,5,11,12,13,15,16,17,18,20],handi:3,handl:[0,1,2,5,9,11,15],handle_unknown:9,handsid:12,handwrit:12,handwritten:[1,5],happen:[1,2,3,4,5,6,10,13,18],hard:[1,7,8,10,13],hardcopi:15,harder:[0,1],harmon:3,hasn:1,hassl:[0,15],hast:15,hasti:[0,6,17,20],hat:[1,5,6,7,9,10,11,12,13,16],have:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],haven:1,he:7,head:[0,4,10,18],header:0,heads_proba:10,health:0,hear:[0,13],heart:[0,7],heatmap:[0,1,3,7],heavili:0,heavisid:1,height:[1,3,6],held:13,help:[0,1,4,12,13],helper:[4,14],henc:[0,5,6,8,9,10,12,13],her:7,here:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20],hereaft:[0,8,12],hermitian:16,hessenberg:16,hessian:[0,2,5,13],heterogen:[9,10],hi:7,hidden:[1,3,4,12],hidden_bia:1,hidden_bias_gradi:1,hidden_layer_s:[0,1],hidden_neuron:4,hidden_weight:1,hidden_weights_gradi:1,hierarch:5,high:[0,1,2,3,4,5,6,9,10,11,13,14,15,16],higher:[0,1,3,5,6,8,13],highest:[1,2],highli:[0,3,4,10,15,16,20],highwai:0,hing:8,hint:13,hip:15,hire:0,hist:[4,6,7,18],histogram:[0,6,7,18],histor:[7,11],histori:[3,4,12],histplot:[],hit:[],hitherto:5,hjorth:19,hobbi:18,hoc:5,hoff:20,hold:[1,3,6,13,14],holder:0,home:0,homework:[6,13],homogen:[1,3,9,10,13],honchar:2,hopefulli:[0,11,18],horizont:11,hors:[3,7],hot:[1,9],hour:[1,15,17,18,19],house_pric:[],how:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,20],howev:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],hs:[],hspace:[0,4,8,10,18],hstack:1,htf:17,html:[7,11,15,17,20],http:[3,4,6,7,11,13,14,15,16,17,20],huang:0,huber:0,huge:[1,3,4,15],human:[0,1,3,6,9,12],humid:9,hundr:1,hungri:1,hybrid:17,hydrogen:0,hyperbol:[1,4,12],hyperparam:8,hyperparamet:[3,4,5,6,9,13],hyperplan:11,i0:0,i1:[0,6,8,12],i2:[0,8,12],i3:[0,12],i5:0,i:[0,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18,19],i_1:[5,6],i_2:[5,6],i_:13,ian:20,ic:1,id:[7,13],ida:19,idea:[0,1,2,3,4,6,9,10,12,13,16],ideal:[0,2,6,8,13,18],idem:6,ident:[5,6,12,13,16],identical:5,identifi:[0,1,7,9,11,12,13,14],idum:[],ieor:18,ifi:20,ifs:15,ignor:[0,1,3,9],ii:[16,18],iii:16,ij:[0,1,3,6,8,12,14,16,18],ik:[0,16],illustr:[5,7,10,12,13,14,15],im:6,imag:[1,3,4,6,9,11,12,14,20],image_at_epoch_:4,image_batch:4,image_height:3,image_path:[0,6,7,9],image_width:3,imageio:6,images_from_seed_imag:4,imagin:1,immedi:[0,3,4,6,15],implement:[0,2,3,4,5,6,8,9,10,11,12,13,14,18],impli:[3,5,6,7,13,16],implicit:3,implicitli:[11,18],importantli:3,impos:[0,6,11,12],imposs:[0,5],impress:[0,12],improv:[0,4,5,9,10,11,13],impur:9,imread:6,imshow:[1,3,4,6],in3050:20,in4080:20,in4300:20,in5400:[3,20],in_out_neuron:4,inaccur:13,inacio:19,inact:12,inadequ:0,inch:6,includ:[0,1,2,3,4,5,6,7,11,12,15,18,19,20],include_bia:[6,9],incom:12,incorrect:1,incoveni:8,increas:[0,1,3,4,5,6,7,8,9,11,12,13,18],increasingli:18,ind:6,inde:[0,2,4,5,6],indefinit:4,indent:[],indentationerror:[],independ:[0,5,6,7,8,12,13,18],index:[0,1,3,4,10,14,15,16,18,20],index_col:0,index_of:[],indic:[0,1,3,4,5,6,9,10,11,13],indispens:6,individu:[1,6,7,10,12,18],indu:0,indx1:2,indx2:2,indx3:2,indx:16,ineffici:[3,13],inequ:8,inequaltii:13,inertia:13,inf1000:15,inf1100:15,inf1100l:15,inf1110:15,inf3000:20,inf4490:20,inf5860:20,infeas:9,infer:[0,1,4,6,20],infer_nrow:0,inferenc:1,infil:[0,6,7,9],infin:[5,6,7,11],infinit:3,infinitesim:18,influenc:[6,10],influenti:1,inform:[0,1,3,4,6,9,11,12,13,14,16,20],infti:[3,6,13,18],ingeni:13,ingrad:2,ingredi:[0,9],inher:6,inherit:16,initi:[0,1,2,6,10,13,14,16,18],initial_epoch:[3,4],initialis:[],initialize_root:[],inject:14,inlin:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],inner:13,innov:20,inp:4,inplac:13,input:[0,1,3,4,5,6,7,8,9,10,12,13,14,18],input_dim:1,input_shap:[3,4],inputs:1,inputs_shuffl:[0,1],insert:[3,5,6,8,10,18],insid:[0,4,7],insight:[0,1,5,15,20],insist:[6,13],inspir:[0,1,12,20],instabl:2,instal:[0,1,5,6,9],instanc:[0,1,2,4,6,9,11,13],instanti:10,instead:[0,1,2,3,4,5,6,8,9,11,13,14,16,18],institut:1,instruct:[0,1],int32:10,int64:[],int_0:18,int_:[3,6,18],int_a:18,intak:0,integ:[1,2,13,14,16,18],integer_vector:1,integr:[3,6,18],intellig:[0,14,20],intend:10,intens:1,intention:14,interact:[0,6,9,12,15],intercept:[0,6,8,11,13],intercept_:[0,6,8,9,13],interchang:[5,12,16],interconnect:1,interest:[0,1,2,3,4,5,6,7,8,9,12,15,17,18],interfac:[0,1,16],interior:[0,9],intermedi:16,intern:[1,10,12],interpol:[1,3,4,6,12],interpr:5,interpret:[0,1,6,9,10,12,13,16,18],interv:[0,3,5,6,7,13,18],intial:13,intract:[0,4],intrins:[3,11,16,18],intro:[15,20],introduc:[0,1,5,6,8,10,12,13,16,18],introduct:[1,2,4,13,17,20],introductori:[0,4,16,20],intuit:[0,5,6,8,12,13],inv:[0,5,13],invalid:1,invalu:[0,13,15],invari:1,invd:5,inver:8,invers:[0,3,6,13],invers_period:[],inverse_transform:8,invert:[0,5,7,10],invok:[0,8],involv:[0,2,6,7,11,12],io:[0,15,17,20],iomanip:[],iostream:[],ip:[0,8,18],ipca:11,ipykernel_42331:[],ipykernel_42376:[],ipykernel_42449:[],ipykernel_42456:[],ipykernel_42530:[],ipykernel_42541:[],ipykernel_42553:[],ipykernel_42573:[],ipykernel_42580:[],ipykernel_42586:[],ipykernel_47411:[],ipykernel_47448:[],ipykernel_47647:[],ipykernel_47724:[],ipykernel_47735:[],ipynb:15,ipython:[0,1,2,3,4,5,6,7,8,9,10,11,13,14,15],iq:6,iri:[8,9],irreduc:6,irrelev:5,irrespect:0,is_integ:[],isbox:[2,13],iscomplexobj:[],isinst:[2,13],isn:5,isnul:0,isomap:11,issu:[1,3,4,9,14,16],it_arrai:13,item:[0,13],items:16,iter:[1,2,3,4,6,7,8,11,13,14,18],itr:5,its:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,20],itself:[5,6,12,18],ix:[],j1:16,j:[0,1,2,3,4,5,6,8,9,11,12,13,14,16,18,20],j_:6,j_lasso_sk:6,j_ridge_sk:6,j_sk:6,jackknavg:[],jackknif:[6,15],jackknstd:[],jackknvar:[],jackknvec:[],jacobian:[2,13],jacobian_shap:2,jargon:[],jason:4,jax:[3,4,14],jensen:19,jerom:20,ji:[12,16],jj:[0,5,6],jk:[0,1,6,12,16],jl:0,jm:16,joao:19,joaogca:19,job:[2,8,10],join:[0,4,6,7,9],joint:[4,5],journal:[],judg:13,judgement:6,julia:[15,16],jump:18,junk:4,jupyt:[0,15,20],just:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,18],justif:0,justifi:[3,10],jy:[],k0:7,k1:7,k:[0,1,3,5,6,7,8,9,10,11,12,13,14,15,16,18],kaggl:6,kappa_d:18,karim:[],karlsen:[],karush:8,keep:[0,1,4,5,6,11,13,14,16],keepdim:[1,6,10,16],kei:[0,1,3,6,12,20],kept:[4,6,14],kera:[0,4,15],kernel:[0,1,3,15],kernel_regular:[1,3],kernel_s:4,kernelpca:11,kev:0,kevin:20,keyboardinterrupt:[2,3,4],keyerror:[],keyword:[6,13,16],kfold:6,kg:1,ki:16,kick:[1,13],kiener:2,kilomet:6,kind:[0,2,3,4,8,12,13,14],kj:[6,12,16],kjm:15,kkt:[5,8],kktsolver:5,kl0m3:17,kl:18,km:12,kmean:14,kmeanspoint:14,kn_k:14,know:[0,1,2,5,6,8,13,15],knowledg:[0,15],known:[1,3,4,5,6,7,8,9,12,16,18,20],kondev:0,kp:18,kpca:11,kroneck:14,kuhn:8,kwarg:[0,2,3,4,13],kwd:[0,3,4],kwown:0,l0:7,l1:[0,1,3,5,7],l1_l2:[1,3],l1regl:5,l1regls_mosek2:5,l1regls_mosek:5,l2:[1,3],l:[0,1,2,3,5,6,7,8,10,11,12,13,16,18],l_1:7,l_2:[7,13],l_:16,l_j:12,la:13,la_i:12,la_k:12,lab:[15,17],label:[0,1,2,3,4,5,6,7,8,9,10,12,13,14,15,16,18],label_prob:13,labelencod:[7,10],labels:[6,8,9],labels_shuffl:[0,1],laboratori:17,lack:0,lagari:2,lagrang:[8,11],lambda:[0,1,2,3,5,6,7,8,10,12,13,18],lambda_0:11,lambda_1:[5,8,11],lambda_2:[8,11],lambda_:11,lambda_i:[8,11],lambda_iy_i:8,lambda_jy_iy_j:8,lambda_k:8,lambda_n:[5,8],lamda:1,lamdbda:5,land:[0,8],landmark:8,landscap:13,langl:[0,6,11,18],languag:[0,1,4,8,15,16,20],lapack:[5,16],laplac:5,laptop:15,larg:[0,1,2,4,5,6,8,9,10,11,13,15,16,18,20],larger:[0,3,5,6,8,10,11,13,18],largest:[4,8,11],lasso:[0,7,15,17],lasso_sk:6,last:[0,1,2,3,4,5,6,7,8,9,10,12,13,16,17,18],latent:4,latent_dim:4,latent_point:4,latent_space_value_rang:4,later:[0,1,4,6,7,8,12,13,14,15],latest:4,latest_checkpoint:4,latter:[0,3,5,6,7,8,11,13,16,17,18],lattic:12,law:0,layer:[0,4,13],lbfg:[7,9,10,11],lcc:[5,6],lda:11,ldot:[0,6,11],le:[5,7,10,13,18],lead:[0,1,3,5,6,7,8,9,10,11,12,13,16,18],leaf:9,leaki:1,leakyrelu:4,lear:13,learn:[3,4,5,6,7,8,9,10,12,16,17,20],learnabl:3,learner:10,learning_r:[3,8,10],learning_rate_init:[0,1],learning_schedul:13,least:[0,2,7,8,10,11,15,16,17,18],leat:13,leav:[0,1,3,5,6,9,11],lectur:[0,1,5,10,11,12,13,15,16,17,20],lecturenot:[15,20],lecturenovember11:[],lecturenovember12:[],lecturenovember19:[],lecturenovember25:[],lecturenovember26:[],lecturenovember4:[],lecturenovember5:[],lectureoctober14:17,lectureoctober15:17,lectureoctober1:[],lectureoctober21:[],lectureoctober22:[],lectureoctober28:[],lectureoctober29:[],lectureoctober7:[],lectureoctober8:[],lectureseptember10:[],lectureseptember16firstpart:[],lectureseptember16secondpart:[],lectureseptember17:[],lectureseptember23:[],lectureseptember24:[],lectureseptember2:[],lectureseptember30:[],lectureseptember3:[],lectureseptember9:[],lecturethursdayaugust26:[],lecturethursdayaugust27:[],left:[0,1,2,3,5,6,7,8,9,10,11,12,13,14,16,18],leftarrow:[8,12],legend:[0,2,3,4,5,6,7,8,9,10,13],len:[0,1,2,3,4,5,6,8,9,10,11,12,16],len_index:0,length:[0,1,2,3,4,8,9,13,15],length_of_sequ:4,leq:[0,5,7,8,13,14,18],less:[0,1,3,4,5,6,8,9,13,18],lessen:1,let:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],letter:[0,16,18],level:[0,1,5,6,9,15,16,17],li:[8,11],lib:[0,1,2,3,4,6,7,8,11,13,14],liblinear:[8,10],librari:[0,1,2,3,4,5,6,9,10,11,16,18,20],licens:[0,1,15],lie:[0,6,11,18],life:[0,1,8,12],lifetim:13,like:[0,1,2,3,4,5,6,7,9,10,11,12,13,15,16,18],likelihood:[0,1,5,9,13],lim_:18,limit:[0,5,6,7,8,11,12,16],lin_clf:8,lin_model:0,lin_reg:9,linalg:[0,2,5,6,8,11,13,16,18],line1:8,line2:8,line2d:13,line3:8,line:[0,1,3,6,8,11,13],linear:[1,3,5,6,7,9,10,11,12,15,17,18],linear_model:[0,5,6,7,8,9,10,11,13],linear_regress:6,linearli:5,linearloc:[6,13],linearregress:[0,6,7,9],linearsvc:8,liner:[1,3],linerar:10,linewidth:[0,2,4,6,8,9,10],link:[0,4,9,12,15],linlag:5,linpack:16,linreg:0,linspac:[0,2,3,4,6,8,9,10,13,16,18],linu:4,linuek:[],linux:[0,1,15],liquid:0,list:[0,1,2,3,4,5,9,15],listcomp:2,listedcolormap:[9,10],literatur:[1,7,14,20],littl:[1,3,9,12],live:8,ll:[0,18],lle:0,lloyd:[4,14],lmb:[2,5,6],lmbd:[0,1,3],lmbd_val:[0,1,3],lmbda:13,ln:[1,13],lo:5,load:[0,1,4,6,7,9,10],load_boston:0,load_breast_canc:[1,7,9,10,11],load_data:[3,4],load_digit:[1,3],load_iri:[8,9],loc:[0,3,6,7,8,9,10],local:[0,1,3,7,12,13],locat:[2,3,8],lock:[3,4],log10:[5,6],log:[0,1,2,4,5,6,7,9,10,11,13,16],log_:0,log_clf:10,logarithm:[0,5,7,16],logic:[0,1,9],logist:[0,1,2,8,9,10,11,12,15,17],logistic_predict:13,logisticregress:[7,9,10,11],logit:7,logreg:[7,9,10,11],logspac:[0,1,3,5,6],longer:[2,3,8,10,14,16,18],loocv:6,look:[0,1,2,3,4,5,6,7,8,9,10,11,13,16,18],loop:[1,4,6,10,12,14,15,16],lose:1,loss:[0,1,3,4,5,6,7,8,10,11,13,16],loss_fil:4,lossfil:4,lost:4,lot:[0,1,4,6],low:[0,6,9,10,11],lower:[0,1,3,6,9,10,16],lowercas:16,lowest:[9,13,18],lr:[1,3,4,10],lstat:0,lstm:4,lstm_2layer:4,lstsq:0,lt:6,lu:[0,5],lubksb:16,luckili:2,ludcmp:16,lux:16,lvert:1,lw:0,m:[0,1,2,3,5,6,8,9,10,11,12,13,16,17,18,19,20],m_1:14,m_:[9,12],m_h:0,m_k:14,m_l:12,m_n:0,m_p:0,m_t:13,ma:11,mac:[3,4,14],machin:[1,3,4,5,6,7,9,10,11,12,14,16,17,20],machinelearn:[6,15,17,20],mackai:20,made:[0,1,3,4,5,6,7,9,11,12],mae:0,magic:4,magnitud:[1,6,7,13],mai:[0,1,2,3,5,6,7,8,9,11,12,13,15,16,18],mail:17,main:[0,1,3,4,5,6,7,9,16,20],mainli:[0,5,6,7,9],maintain:6,major:[1,6,9,10,13,16],make:[1,2,3,4,5,6,7,8,11,12,13,15,16,18,20],make_axes_locat:6,make_moon:[8,9,10],make_pipelin:[0,6,10],make_vjp:[2,13],makedir:[0,6,7,9],makeplot:0,malcondit:16,malign:[1,7,9],mammographi:5,manag:[0,2,3,15],mani:[0,1,3,4,5,6,7,8,9,11,13,14,15,16,18,20],manifold:11,manner:3,manual:6,map:[0,1,2,6,7,8,11,12,14,18],margin:[0,5,8],mari:19,marit:0,marker:[0,7,16],markov:15,marsaglia:18,mask:[],mass:[0,1,5,13],massag:0,masses2016:0,masses2016ol:0,masses2016tre:0,masseval2016:0,master:[6,17],mat1100:15,mat1110:15,mat1120:15,mat3155:[],mat4155:[],mat:15,match:[0,1,4,5,13,14],materi:[4,5,7,13,16],math:[3,7,12,13,16,18,20],mathbb:[0,4,5,6,7,8,11,12,13,14,16,18],mathbf:[0,5,6,7,8,13,16],mathcal:[1,5,6,7,13],matheemat:3,mathemat:[0,6,11,12,13,15,16,17,18,20],mathrm:[0,1,3,4,5,6,7,8,9,10,11,12,13,14,18],matmul:[1,2,5],matmul_adjoint_1:2,matmul_vjp_0:2,matmul_vjp_1:2,matnat:[17,19,20],matplotlib:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],matplotlibdeprecationwarn:[6,13],matric:[0,1,3,4,6,7,8,11,13,15],matrix:[0,2,3,4,6,7,8,10,13,18],matshow:1,matter:[2,3,13],max:[0,1,2,3,4,9,10,12,13],max_depth:[0,9,10],max_diff1:2,max_diff2:2,max_diff:2,max_it:[0,1,7,8,11,13],max_iter:14,max_leaf_nod:10,max_queue_s:[3,4],max_sampl:10,maxdegre:[0,6,10],maxdepth:10,maxim:[1,4,5,7,8,11],maximum:[0,1,2,3,5,7,8,9,10,13,14],maxpolydegre:[5,6],maxpooling2d:3,mbox:[5,6],mc:[],mcculloch:12,mcint:[],mcintsqr2:[],md:11,mdoel:4,mean:[1,2,3,4,5,6,7,9,10,11,12,13,14,15,16,18],mean_absolute_error:0,mean_divisor:14,mean_i:18,mean_matrix:14,mean_squared_error:[0,4,6,7,10],mean_squared_log_error:0,mean_vector:14,mean_x:18,meaning:[0,4,7],meansquarederror:0,meant:[2,3,7,10,13],meantempvec:[],meanvec:[],measur:[0,1,2,5,6,9,11,12,14,18],mechan:[0,4,18],median:0,medicin:12,medium:[4,8,13],medv:0,meet:[0,19],mehta:0,memori:[3,4,11,12,13,16],mention:[0,12,13,18],mere:0,mersienn:[],meshgrid:[2,5,6,8,9,10,11],messag:[5,13],messi:2,met:[0,3,8],metadata:2,meteorolog:9,meter:6,method:[0,1,2,3,4,5,7,8,11,12,14,15,16,17,18,20],metion:6,metric:[0,1,3,6,7,9,10,14],metropoli:15,mev:[0,18],mgd:13,mglearn:15,mgrid:13,mhjensen:[1,2,3,4,6,7,8,11,14],mi:10,microsoft:20,mid:1,midel:4,midpoint:9,might:[0,1,2,4,6,9,13],mild:9,miller:[],millimet:6,million:0,mimic:12,min:[0,2,5,8,9],min_:[0,2,5,14],min_samples_leaf:9,mind:[0,6,13],mindboard:4,mine:15,mini:[1,11,12,13],minibatch:[1,11,13],minibathc:13,miniforge3:[0,1,2,3,4,6,7,8,11,13,14],minim:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],minima:[0,1,7,13],minimum:[0,1,2,6,8,9,11,13],minmaxscal:0,minor:[6,13,18],minst:1,minu:7,mirror:9,misc:6,misclassif:[8,9,10],misclassifi:[8,10],miser:0,mismatch:1,miss:[0,10],mistak:4,mit:20,mix:[1,2],mixtur:13,mk:[9,16],mkdir:[0,6,7,9],ml:[0,1,10,13,16],mlab:18,mle:[5,7],mline:[],mlir:[],mlir_graph_optimization_pass:[],mlp:1,mlpclassifi:1,mlpregressor:0,mm:16,mn:[12,18,20],mnist:[1,11],mod:18,mode:17,model:[2,3,5,7,8,9,10,11,13,14,15,18,20],model_select:[0,1,3,5,6,7,9,10,11],moder:10,modern:[0,6,7,15],modif:[2,12,13],modifi:[0,1,3,5,7,8,10,12,13],modul:[0,2,3,4,5,6,7,8,9,10,11,13,16],modular:18,modulenotfounderror:[5,7,8,9],modulo:18,moe:[5,11],moment:[5,6,13,18],monitor:13,monoton:[5,12,18],mont:[0,6,15,18,20],montecarlocycl:[],moor:[5,6],more:[0,1,2,4,5,7,8,9,10,11,12,13,14,15,17,18],moreov:[0,3],morten:19,mortenimac:[],mosek:5,most:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,17,18],mostli:[1,11],motion:[0,13],motiv:[1,4],move:[0,4,5,6,7,9,12,13,14,18],mp4:17,mpl:[0,7],mpl_toolkit:[2,6,13],mplot3d:[2,6,13],mplregressor:1,mse:[0,4,5,6,9,10],mse_simpletre:10,mselassopredict:5,mselassotrain:5,mseownridgepredict:6,msepredict:5,mseridgepredict:[5,6],msetrain:5,msg:[],msle:0,mt19937_64:[],mt:[7,12],mu0:18,mu1:18,mu2:18,mu:[0,6,11,13,18],mu_:[6,18],mu_i:6,mu_n:11,mu_x:18,much:[0,1,2,3,4,5,6,8,9,10,11,12,13,16,18],mul:5,multi:[0,1,3,7,15],multiclass:[1,7],multidimension:[11,12],multilay:1,multinomi:7,multipl:[2,4,5,6,7,12,13,18],multipli:[3,5,6,11,13,16,18],multiplum:8,multivari:[0,2,10,11,15,18],multivariate_norm:[11,14],murphi:[11,20],must:[0,1,2,5,6,8,10,12,13,14,18],mutat:7,mutual:[1,3,6,13],mx_:18,myenv:[0,1,2,3,4,6,7,8,11,13,14],myriad:[0,15],mz1:18,mz2:18,n1:16,n2:16,n:[0,1,2,3,4,5,6,7,8,10,11,12,13,14,16,18],n_0:[12,18],n_:[1,2,3,8,12,18],n_b:[],n_boostrap:[6,10],n_bootstrap:6,n_categori:[1,3],n_cluster:14,n_compon:11,n_epoch:13,n_estim:10,n_examples_to_gener:4,n_featur:1,n_filter:3,n_hidden:2,n_hidden_neuron:[0,1],n_i:18,n_input:[0,1,3],n_instanc:9,n_iter_i:[7,11],n_job:10,n_k:14,n_l:[12,18],n_layer:1,n_m:9,n_neuron:1,n_neurons_connect:3,n_neurons_layer1:1,n_neurons_layer2:1,n_point:14,n_sampl:[6,8,9,10,14],n_split:6,n_step:4,n_t:2,n_x:2,nabla:[1,13],nabla_:[2,13],nabla_w:13,nag:13,naimi:0,naiv:7,naive_kmean:14,nall:0,name:[0,1,3,4,5,6,7,8,9,10,12,13,14,15,16,18,19],nameerror:[6,10],namespac:[],narrow:13,nary_f:[2,13],nary_op_arg:[2,13],nary_op_kwarg:[2,13],nary_oper:[2,13],nation:[1,5],nativ:15,natur:[0,1,4,8,9,12,13,18,20],navier:12,nb:18,nb_:16,nboot:[],nd:14,ndarrai:[2,6],ndim:2,ne:[9,10,16,18],nearest:[1,3,6,11],nearli:13,neccesari:6,necess:2,necessari:[0,1,3,4,8,14],necessarili:[0,4,11,18],necesserali:5,neck:7,need:[0,1,2,3,4,5,6,8,9,10,11,12,13,14,16,18],neg:[0,1,3,5,6,7,10,13,16,18],neg_mean_squared_error:6,neglect:18,neglig:18,neighbor:[3,6,11],neither:[4,13],neq:[13,14,18],nervou:12,nest:[2,9,12],nesterov:13,net:[2,4,12],netlib:16,network:[0,9,13,15,17,20],neural:[0,7,13,15,17,20],neural_network:[0,1,2],neuralnetwork:1,neuron:[1,2,3,4,12],neutral:0,neutron:0,never:[1,3,4,6,9,18],new_box:[2,13],new_root:[2,13],new_trac:[2,13],new_tracing_count:[3,4],newaxi:[0,3,6,9],newli:0,newton:[1,7,8,13,18],next:[0,1,2,3,4,5,6,8,9,13,14],next_guess:13,next_input:4,ng:1,ngini:[],ni:14,nian:[],nice:[0,1,5,11],nichola:[],nicholaskarlsen1102:[],niter:13,nitric:0,nlambda:[5,6],nlevel:[],nm:18,nm_n:0,nmse:6,nn:[2,5,6,12,16],nn_model:1,nnmin:2,node:[1,2,3,9,10,12],node_constructor:2,nois:[0,4,5,6,8,9,10,13],noise_dimens:4,noisi:[1,6],non:[0,1,3,4,5,6,7,9,10,11,12,13,14,16,18],none:[0,1,2,3,4,5,9,10,13,18],nonlinear:[3,6,8,9,11,12],nonneg:[6,9,13],nonparametr:6,nonsens:18,nonsingular:16,nonumb:[3,7,8,13,16],nor:[1,4,13],norm:[0,1,5,6,8,11,13],normal:[3,4,5,6,7,8,9,10,11,12,13,15,16,18],normali:16,normalize_kwarg:[],norwai:6,notat:[0,2,5,6,13,14,18],note:[0,1,2,3,4,5,6,7,8,11,12,13,14,15,16,17,18,20],notebook:[0,1,3,9,15],noth:[1,2,5,8,12,14,18],notic:[4,5,12,13,16,18],notimplementederror:[],notion:3,notrace_primit:[],novel:[3,6,10],novemb:1,now:[0,2,4,5,6,7,8,10,11,12,13,14,16,18],nowadai:[0,1,3,9,15],nox:0,np:[0,1,2,3,4,5,6,7,8,9,10,11,13,14,16,18],npr:2,nsampl:6,nspin:[],nt:2,nthi:0,ntrained_model:6,nu:18,nuclear:5,nuclei:[0,18],nucleon:0,nucleu:0,num:4,num_coordin:2,num_hidden_neuron:2,num_it:2,num_neuron:2,num_neurons_hidden:2,num_output:[3,4],num_point:2,num_tre:10,num_valu:2,number:[1,3,4,5,6,7,8,9,10,11,12,13,14,16,17,19],numberid:7,numberparamet:3,numer:[0,5,6,9,10,11,12,13,15,16,20],numpi:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,18],numpy_vjp:2,numpy_wrapp:2,nunmpi:5,nvalu:[],nx:2,nx_test:6,nx_train:6,nx_train_mean:6,ny:18,ny_pr:6,ny_train:6,ny_train_mean:6,o:[0,6,7,8,9,11,16,20],obei:[6,11,13],object:[0,1,2,4,5,6,8,10,13,16],objsens:5,obliqu:5,observ:[1,3,5,6,7,8,9,10,11,12,13,14,18],obtain:[0,1,5,6,7,8,9,10,12,13,14,16,18],obviou:[5,6,11,18],obviouli:0,obvious:[0,4,5,6,16],oc:5,occupi:0,occur:[0,6,8,9,16,18],od:0,odd:[0,3,7],odenum:2,odesi:2,oen:0,off:[1,3,4,5,9,13,18],offer:[6,11,15,16,17],offic:19,offici:17,ofil:[],ofstream:[],often:[0,1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],ofter:16,ol:0,old:[1,5,10,13],ols_sk:6,ols_svd:6,olsbeta:5,omega:[2,3,6],omega_0:3,omit:[0,5],on_train_batch_begin:[3,4],onc:[1,6,9,11,13],one:[0,1,3,4,5,6,7,8,9,10,11,13,14,15,16,18],oneapi:[],onednn:[],onehot:1,onehot_vector:1,onehotencod:9,ones:[0,2,5,6,8,9,10,11,13,16,17],ones_lik:4,onl:3,onli:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],onlin:[11,17],onto:[5,11],op_nam:[3,4],open:[0,1,4,6,7,9,15,17],oper:[0,1,3,5,6,10,11,12,13,15,18],operation:18,oplu:18,opmiz:13,opportun:0,oppos:[6,13],opposit:[1,5,8],opt:[1,5],optim:[0,2,3,4,5,6,7,9,10,11,14,17],optimis:[1,3],optimizer_v2:3,option:[0,1,3,5,6,7,8,11,16],optionalxlacontext:[3,4],optmiz:[1,8,13],orang:0,order:[0,1,2,3,5,6,7,8,9,10,11,12,13,16,18],ordinari:[0,2,3,7,11,13,15,17],oreilli:20,org:[3,4,7,11,15,16,20],organ:[6,7,10,16],orient:[1,5,18],origin:[0,3,5,6,8,11,12,13,16],orthogn:5,orthogon:[0,5,6,8,11,13,16],orthonorm:5,os:[0,1,4,5,6,7,8,9],oscar:1,oscil:[3,13],oslo:[0,15,17,19],osx:[0,15],other:[0,1,2,3,5,6,7,8,10,13,14,15,17,18,20],otherwis:[0,1,4,7,13,16],ouput:[5,7,12],our:[1,2,3,6,7,8,9,10,12,14,15,16,17,18],ourmodel:0,ourselv:[0,5,6,8,11,13],out:[0,1,2,4,5,6,7,8,9,10,11,12,13,15,16,18],out_fil:9,outcom:[0,7,9,10,12,18],outdoor:9,outer:[6,12],outfil:4,outfilenam:[],outgrad:2,outlier:[0,8],outlin:[6,10,11],outlook:9,outperform:10,output:[0,1,3,4,5,6,7,8,9,10,12,13,16,18],output_bia:1,output_bias_gradi:1,output_shap:4,output_weight:1,output_weights_gradi:1,outputlayer1:12,outputlayer2:12,outsid:4,over1:13,over:[0,1,3,4,5,6,9,10,12,13,16],overal:[1,10],overcast:9,overcom:[12,13],overdetermin:0,overfit:[0,1,3,6,9,10,13],overflow:[1,5],overhead:12,overlap:[3,7,8,9],overlin:[0,5,6,9,10,11,14,16],overst:0,overtrain:4,overview:[3,20],own:[4,5,6,8,12,13,15,16],owner:0,ownridgebeta:6,oxid:0,oyvinssc:19,p0:2,p1:2,p:[0,1,2,3,4,5,6,7,8,9,10,11,13,14,16,18],p_:[2,4,8,9],p_hidden:2,p_i:[5,18],p_j:18,p_n:18,p_output:2,p_x:18,pack:0,packag:[0,1,2,3,4,5,6,7,8,11,13,14,15,18],pad:[3,4],page:[0,15],pai:[0,1,9,13],pair:[0,2,3,9,15,18],panda:[0,4,5,6,7,9,11,15],paper:1,paradigm:0,parallel:[10,16],param:[2,4],paramat:2,paramet:[0,1,2,3,4,5,6,7,8,9,10,12,13,18],parameter:[0,6,10],parametr:[0,6],paramt:[3,5],parent:2,parent_argnum:[],park:[],parser:0,part:[0,1,3,5,6,10,16,17,18,20],partial:[0,1,5,6,7,8,10,11,12,13,18],particip:[15,17],particl:[0,4,13,18],particular:[0,1,2,3,5,6,9,10,11,12,13,18,20],particularli:[5,6,8,11,13,18],partit:[1,4,9],partli:6,pass:[2,3,12,14],past:[10,18],patch:[6,18],path:[0,4,6,7,9,15],patient:7,patter:4,pattern:[0,3,4,12,17,20],pauli:0,pc:[11,15],pca:[0,7,15,17],pcolor:6,pcolormesh:6,pd:[0,4,5,6,7,9,11],pde:2,pdf:[0,3,4,5,6,9,20],pedagog:0,penal:6,penalti:[6,13],penros:[5,6],pentagon:13,peopl:[0,1,9,13,15],per:[0,1,6,17],percentag:[0,10,11],perceptron:[0,1,7],perfect:[0,1],perfectli:[4,6],perform:[0,2,3,4,5,6,8,10,11,12,13,14,15,16,18],performac:4,perhap:[0,5,13],perimet:1,period:[1,4,18],permut:11,persist:13,person:[5,6,7,17,19],perspect:20,pertin:12,petal:[8,9],peter:20,petersen:[],phantom:18,phase:[6,12],phenomena:18,phi:8,phi_k:8,philip:[],philosophi:13,phone:19,photo:4,phrase:0,physic:[0,1,4,7,12,13,18,19,20],pi:[2,3,5,6,7,9,12,13,18],pick:[1,9,10,11,13,14],pickl:1,pictur:0,pie:15,piec:[11,14],pillow:[0,15],pinv:[5,6,13],pip3:[0,1],pip:[0,1,15],pipelin:[0,6,8,10],pit:4,pitfal:6,pitt:12,pixel:[1,3,4],pixel_height:[1,3],pixel_width:[1,3],place:[0,4,6,8,13,16],plai:[0,3,4,5,6,8,11,15],plain:[8,10,12,13,14],plan:[6,9,19,20],plane:[8,9],plateau:5,platform:15,plausibl:12,pleas:[3,4,6,7,11,13,14],plenti:1,plethora:[3,12],plot:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],plot_confusion_matrix:[7,10],plot_count:6,plot_cumulative_gain:[7,10],plot_data:1,plot_dataset:8,plot_decision_boundari:[9,10],plot_import:10,plot_max:4,plot_min:4,plot_model:4,plot_numb:4,plot_predict:8,plot_regression_predict:9,plot_result:4,plot_roc:[7,10],plot_surfac:[2,6,13],plot_train:9,plot_tre:[9,10],plt:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],plu:[0,3,5,7],pm:8,pmatrix:2,pn:3,png:[0,4,6,7,9],point:[0,1,2,3,5,6,7,8,9,10,11,13,14,16,18,19],point_1:4,point_2:4,poisson:[15,18],poli:[6,8],poly100_kernel_svm_clf:8,poly3:0,poly3_plot:0,poly3dcollect:13,poly_featur:[8,9],poly_features10:9,poly_fit10:9,poly_fit:9,poly_kernel_svm_clf:8,polydegre:[0,5,6,10],polygon:13,polym:12,polynomi:[0,5,6,7,8,9,10,11],polynomial_featur:6,polynomial_svm_clf:8,polynomialfeatur:[0,6,8,9],polytrop:[0,6],pool:3,pool_siz:3,poor:[1,13],poorli:0,pop:2,popul:[0,5],popular:[0,1,3,6,7,8,9,11,12,15,16,18],popularli:0,portabl:10,portion:[11,13],pose:[0,4,5,6,11,18],posit:[0,1,2,3,5,7,8,10,11,13,14,16,18],possibl:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,18,19],posterior:5,postpon:0,postul:5,potenti:[0,3,5,6,12,13],potr:5,potrf:5,pott:12,power:[0,1,5,6,8,9,12,13],pp:[5,6],practic:[0,5,6,7,8,17,18],practition:[0,1,3],preced:[1,11,12,18],preceed:4,preceq:8,precis:[0,2,5,11,13,16,18],pred:[6,13],predicit:0,predict:[0,1,5,6,7,8,9,10,15,20],predict_prob:1,predict_proba:[7,10],predictor:[0,5,6,7,9,10,11],prefer:[0,1,6,8,9,11,15],prepar:[0,6,16],preprocess:[0,4,6,7,8,9,10,11],prerequisit:0,presenc:13,present:[0,5,6,9,12,13,16,17,18],preserv:[3,11,16],press:[13,20],pretrain:[1,4],pretti:[0,4,8,9,15],prev_centroid:14,prevent:[13,18],previou:[0,1,2,3,4,5,6,8,10,11,12,13,16,18],previous:[2,3,9,10,18],price:[0,4,9,13],primal:8,primari:[0,7],prime:18,primit:2,princip:[0,5,7,15,17],principl:[0,6,7,8,14],print:[0,1,2,3,4,5,6,7,8,9,10,11,13,14,16,18],print_funct:[8,9],printout:0,prior:[0,5,6],privat:0,prob:[1,18],probabilist:[0,20],probabl:[0,1,3,4,6,7,10,13,15,17],problem:[0,3,4,5,6,7,8,9,10,11,12,14,15,16,17,18],proce:[0,5,6,8,9,10,11,13,16],procedur:[2,4,5,6,8,10,11,13],proceed:16,process:[0,2,4,6,9,10,12,13,15,16,18,20],prod:20,prod_:[1,5,7],produc:[0,3,4,5,6,9,10,11,12,13,15,16,18],product:[0,1,3,5,6,7,8,12,13,15,16],profess:0,program:[0,1,4,5,6,8,12,14,15,16,17,18],programm:16,progress:[1,4,14],prohibit:6,project1:6,project:[0,1,2,3,5,11,13,15,17,19],project_root_dir:[0,6,7,9],promin:12,promis:8,prone:9,pronounc:[13,15],proof:[0,11,12,13],propag:[2,3,13,17],proper:[0,2,6],properli:[1,6,8,10,13],properti:[0,1,3,12,13,16],proport:[0,1,5,9,11,13,18],propos:[1,4,6,10],propto:[5,13],protect:[3,4],proton:0,prove:[3,13],provid:[0,1,3,4,5,6,8,9,10,12,13,15,16,18,20],proxi:[1,13],prune:9,pseudo:[16,18],pseudoinv:5,pseudoinvers:[5,6],pseudorandom:[6,18],psycholog:0,pt:13,ptratio:[],punish:[0,1],pure:[3,9,18],purest:9,puriti:9,purpos:[0,3,10,12,14],put:1,putarow:5,putboundslic:5,putclist:5,putobjsens:5,putqobj:5,py:[0,1,2,3,4,5,6,7,8,11,13,14],pydata:15,pydot:9,pylab:[0,7],pylint:[3,4],pypi:15,pyplot:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],pythagora:5,python2:0,python3:[0,1,2,3,4,6,7,8,11,13,14,15],python:[1,2,3,4,5,6,8,11,12,13,14,17,18],pytorch:[0,15],pywrap_tf:[3,4],q:[5,6,8,11,18],qp:8,qquad:[2,11,13,16],qr:[5,6,16],quad:[1,13,16],quadrat:[0,5,8,9,13],qualit:[4,9,18],qualiti:[0,9,15],quantifi:1,quantil:10,quantit:[0,6,9],quantiti:[0,2,5,6,7,9,10,11,12,14,16,18],quantum:[4,12],quartil:0,quench:5,queri:9,question:[0,5,6,9,11,12,13],qugan:4,quick:[4,18],quick_execut:[3,4],quickli:[1,3,9,11,13],quit:[1,5,6,9,10,12],quot:4,r2:[0,5,6],r2_score:0,r2score:0,r:[0,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],r_1:9,r_2:9,r_j:9,r_m:9,rad:0,radial:[0,8,12],radioact:18,radiu:[0,1],rag:2,rain:9,rais:[0,2,13],ramp:1,ran0:18,ran1:18,ran2:18,ran3:18,rand:[0,4,5,6,9,10,13,16],rand_max:[],randint:[6,9,13],randn:[0,1,2,5,6,9,11,13],random:[0,1,2,3,4,5,6,8,9,13,14,15,16,17],random_devic:[],random_forest_model:10,random_index:13,random_indic:[1,3],random_st:[0,7,8,9,10,11],randomforestclassifi:10,randomli:[1,6,9,13,14],randomnumbergener:[],rang:[0,1,2,3,4,5,6,7,9,10,11,12,13,14,16,18],rangl:[0,6,11,18],rangle_x:18,rank:5,rankdir:4,raphson:[1,8,13],rapidli:0,rare:[1,13],rate:[0,1,2,3,4,8,9,10,12,13],rather:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,16,18],ratio:[4,7,9,10,11],rational:0,ravel:[5,6,7,8,9,10,11,13,16],raw:3,raw_df:[],rbf:[8,11,12],rbf_kernel_svm_clf:8,rbf_pca:11,rc:[0,18],rcond:0,rcparam:[0,1,3,7,8,9,10,18],rd:[],re:[2,4,13],reach:[1,4,5,6,7,9,10,11,12,13,14],read:[0,2,3,4,5,6,7,8,11,12,16,17,18,20],read_csv:[0,6,7,9],read_fwf:0,reader:[0,6,16,18],readi:[0,1,5,6,8,10,11,12,16],readili:1,real:[0,1,2,4,7,10,11,12,13,16],real_loss:4,real_output:4,realist:8,realiti:18,realiz:[1,12],realli:[0,1],rearrang:13,reason:[0,1,3,4,10,13,20],reassign:1,rebuild:[],recal:[5,6,9,10,11,12,16,18],recalcul:[],recast:3,receiv:[1,3,10,12,18],recent:[0,2,3,4,5,6,7,8,9,10,13],recept:[3,12],receptive_field:3,recip:[0,6,7,16],reciproc:5,recogn:[0,4,5,10],recognit:[0,1,3,12,17,20],recommend:[0,2,3,4,5,6,8,13,15,16,17,20],reconsid:9,reconstruct:11,record:[10,17],recreat:[],rectangl:[9,13],rectangular:5,rectifi:[1,3,12],recur:[0,15],recurr:[0,1,15,17],recurs:[9,15,16],recycl:[],red:[0,3,4,6,8,9],redefin:[0,10],reduc:[1,3,5,6,9,10,11,13],reduct:[0,10,11,15,18],refer:[0,1,2,3,5,6,7,11,12,13,14,16,20],referenc:2,refin:12,refit:6,reflect:[0,1,4,5,18],refresh:[15,17],reg:[10,11],regard:[1,9,13],regardless:12,region:[3,4,6,9,12],regist:[6,18],reglasso:5,regr_1:[0,9],regr_2:[0,9],regr_3:[0,9],regress:[1,8,11,12,15,16,17],regressor:[0,7,10],regridg:[5,6],regular:[0,3,4,5,6,7,9,13],regularis:6,reilli:[0,20],reinforc:[0,8,15],reiter:1,rel:[0,4,6,7,9,12,13,18],relat:[0,1,3,4,5,11,13,14,16,18],relationship:[0,4,9],relativeerror:0,releas:[1,3,4,6,13,15],relev:[0,1,5,7,11,15,17,18],reli:[0,6,8],reliabl:[7,18],relu:[3,4],remain:[1,2,4,6,12,16,18],remaind:18,reman:2,remark:1,rememb:[0,8,13,16],remind:[0,5,11,13,16,18],remov:[0,4,5,6],render:0,reorder:[5,7],reorgan:0,repeat:[0,1,3,4,5,6,9,10,11,13,14,16,18],repeated:0,repeatedli:[6,10,13],repet:3,repetit:[6,17],rephras:13,replac:[0,1,3,4,5,6,10,12,13,14],replica:6,repositori:[0,4],repres:[0,1,2,3,4,5,6,7,8,9,10,12,13,18],represent:[0,1,3,6,18],representd:3,reproduc:[0,5,6,9,12,15,18],repuls:0,request:[0,13],requir:[0,1,3,4,5,6,8,9,11,12,13,16],res1:2,res2:2,res3:2,res_analyt:2,res_analytical1:2,res_analytical2:2,res_analytical3:2,resaml:6,resampl:[0,7,10,15,17],rescal:[0,11,12],rescu:5,reseach:6,research:[0,4,15,20],resembl:[6,18],reserv:[1,5,6,18],reset:[],reshap:[0,1,2,3,4,6,8,9,10,16],residenti:0,residu:[0,5,13],resiz:5,respect:[0,1,2,3,5,6,7,8,10,11,12,13,14,18],respond:12,respons:[0,7,9,12],rest:[0,5],restat:[0,12],restor:4,restored_discrimin:4,restored_gener:4,restrict:[0,3,9,12],result:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],result_typ:[],retail:0,retain:[5,6],return_data:14,return_kwarg:[],return_sequ:4,return_x_i:9,reus:[1,3,6],reveal:[0,12],revers:[1,16],review:[15,16,17],revisit:14,reward:[0,4],rewrit:[0,3,5,6,7,8,10,11,12,13,16,18],rewritten:[2,6,8,10,18],rewrot:13,rf:10,rgb:3,rgoj5yh7evk:15,rh:[5,6],rho:[0,10],rho_1:10,rho_2:10,rho_m:10,rich:0,ride:9,rideclass:9,ridedata:9,ridg:[0,7,11,13,15,17],ridge_sk:6,ridgebeta:5,right:[0,1,2,3,5,6,7,8,9,10,12,13,14,16,18],right_sid:2,rightarrow:[0,1,5,6,8,11,12,13,18],rigor:0,ring:6,rise:0,risk:[0,13],rival:4,river:0,rm:[0,18],rmse:0,rmsporp:13,rmsprop:[1,3,4,13],rnd_clf:10,rng:18,rnn1:4,rnn2:4,rnn:[4,12,17],rnn_2layer:4,rnn_input:4,rnn_output:4,rnn_train:4,rntrick1:18,rntrick2:18,rntrick3:18,rntrick4:18,ro:[0,13],robert:20,robust:0,robustscal:0,roc:10,role:[0,2,5,6,8,15],roll:6,room:[0,19],root:[0,5,9,13,18],rotat:[1,8,9,10],rotation_matrix:9,roughli:[1,3],round:[0,7,9,13],routin:[13,16],row:[0,1,2,5,6,7,9,11,16],rr:5,rrr:5,rthe:5,rug:13,rule:[0,1,5,6,13],run:[0,1,2,3,4,5,6,8,9,11,13,15],runtim:[1,6,14],runtimewarn:[1,6],rust:[0,15,16],rustad:19,rvert:1,rvert_2:1,s:[0,1,2,3,4,5,6,7,9,11,12,13,15,16,17,18,19],s_1:6,s_:[3,6],s_i:[6,7],s_j:6,s_k:6,saddl:13,safe:[],sai:[0,1,2,3,4,5,6,7,8,9,10,11,12,16,18],said:[6,9,13],sake:[0,5,7,11],sale:0,same:[0,1,2,3,4,5,6,8,9,11,12,13,14,16,18],samm:10,sampl:[0,1,2,3,4,5,6,7,8,9,10,13,14,15,16,18],sample_vari:14,sample_weight:[3,4],sampleexptvari:18,sastri:11,satisfactori:0,satisfi:[1,2,3,6,8,13,16,18],satur:[1,6],save:[0,4,6,7,9],save_fig:[0,6,7,9,10],savefig:[0,4,6,7,9,18],savetxt:4,saw:5,scalabl:10,scalar:[2,5,6,10,13],scale:[0,1,3,5,6,7,8,9,10,11,12,13,15,16,19],scale_mean:4,scale_std:4,scalei:[],scaler:[0,7,8,9,10,11],scalex:[],scan:[5,7],scari:5,scatter:[0,1,6,7,8,9,14],scenario:[6,13],schedul:13,scheme:[1,13],schrage:18,scienc:[0,1,10,12,13,15,17,18,20],scientif:[0,15],scientist:0,scikit:[3,5,6,7,8,9,10,13,15,16,17,20],scikitplot:[7,10],scipi:[0,3,5,6,13,15,16],scl:6,score:[0,1,3,6,7,9,10,11,19],scores_kfold:6,scratch:1,sdg:13,seaborn:[0,1,3,6,7],seamless:[0,15],search:[0,1,3,5,9,13],sec:6,second:[0,2,3,4,5,6,7,8,9,11,12,13,14,15,16,18],secondeigvector:11,secondli:12,section:[4,11,16,17,18],sector:0,see:[0,1,2,3,4,5,6,7,8,10,11,12,13,14,15,16,17,18],seed:[0,1,2,3,4,5,6,8,9,11,13,14,18],seed_imag:4,seek:[1,2,8],seem:[1,3,4],seemingli:0,seen:[0,1,3,5,10,12,18],segment:13,seismic:6,seldomli:0,select:[1,5,6,8,9,10,11,17,18,20],self:[1,3,4,5,20],sell:4,semest:[7,17],semi:[8,13],semilogx:6,send:[5,12,13,19],senior:17,sens:[0,4,6,8],sensibl:3,sensit:[0,5,6,9,13],sent:2,sentenc:[4,12],sep:[],separ:[0,1,2,4,6,8,9,12,14,15,18],sequenc:[2,3,4,7,9,10,12,13,15,16,18],sequenti:[1,3,4,10,12,18],seri:[0,1,2,3,4,5,6,10,11,12,13,16,17],serif:[0,7,18],serv:[0,1,2,3,5,7,13,20],session:[1,17],set:[1,4,5,6,7,8,10,11,13,14,15,16,18],set_major_formatt:6,set_major_loc:6,set_stream:5,set_tick:[1,8],set_ticklabel:1,set_titl:[0,1,2,3,7,12,14],set_xlabel:[0,1,2,3,7,12],set_xlim:[7,12],set_xticklabel:1,set_ylabel:[0,1,2,3,7],set_ylim:[7,12],set_ytick:7,set_yticklabel:[1,6],set_zlim:6,seth:4,setiosflag:[],setminu:6,setosa:[8,9],setosa_or_versicolor:8,setp:6,setprecis:[],setse:5,setup:[1,4,5,6,8,15],setw:[],sever:[0,3,5,6,7,8,9,11,12,13,15,16,17,18],sgd:[1,3],sgd_clf:8,sgdclassifi:8,sgdreg:13,sgdregressor:13,sgn:5,sh:[],shallow:13,shape:[0,1,2,3,4,5,6,7,8,9,10,11,13,14,16],share:[1,3],she:7,shift:[1,6,12,18],ship:3,shortcom:13,shorten:4,shorter:18,shortli:16,should:[0,2,3,5,6,8,9,11,12,13,16,18],should_sync:[3,4],show:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],show_shap:4,shown:[0,4,5,7,8,11,12,13,16],showpoint:[],shrink:[3,5,6,8,11],shrinkag:[5,6],shrunk:11,shuffl:[0,1,3,4,6,13],side:[0,2,5,8,12,13,16],sigh:15,sigma0:18,sigma1:18,sigma2:18,sigma:[0,1,5,6,7,10,11,12,13,16,18],sigma_0:5,sigma_1:5,sigma_2:5,sigma_:[5,16],sigma_fn:[7,12],sigma_i:[0,5],sigma_j:5,sigma_m:[6,18],sigma_n:[11,18],sigma_t:13,sigma_x:18,sigmoid:[1,2,4,7,8,10,12,13],sigmundson:[6,19],sign:[1,2,7,8,10,18],signal:[1,3,10,12],signatur:[3,4],signifi:4,signific:1,significantli:[1,13,18],sigurd:19,sim:[4,5,6,13,18],similar:[0,1,2,3,4,5,6,7,8,9,10,11,13,14,15,16],similarli:[0,1,3,5,8,10,18],simpl:[1,2,3,5,6,7,8,10,11,12,14,15,16,18],simple_rnn:4,simplepredict:10,simpler:[0,1,5,6,13,15],simplernn:4,simplest:[0,1,3,4,9,10,12,14],simpletre:10,simpli:[0,1,2,4,5,6,8,9,10,11,12,15,16,18],simplic:[2,5,6,7,8,9,10,11,12,14],simplicti:5,simplifi:[0,6,9,15],simplist:[3,6,18],simul:6,simultan:6,sin:[0,1,2,3,4,9,12,13,16],sinc:[0,1,2,3,5,6,7,8,9,10,11,13,16,18,20],sine:[3,12],singl:[0,1,2,3,5,6,7,8,9,12,13,16,18],singular:[0,6,13,16,17],sinusoid:3,site:[0,1,2,3,4,6,7,8,11,13,14,17],situat:[0,4,5,7,13,18],six:[3,18],size:[0,1,2,3,4,5,6,8,9,10,11,13,16,18],sketch:10,ski:9,skill:0,skip:[3,4,11],skiprow:[],skl:[0,6],sklearn:[0,1,3,5,6,7,8,9,10,11,13,14],skplt:[7,10],sl:6,slack:8,slice:[2,16],slide:[0,3,18],slight:[6,13],slightli:[1,2,3,5,6,7,10,18],slope:[8,11,12],slow:[0,2,8,13],slower:[5,16],slowest:16,slowli:12,slp:1,small:[0,1,2,3,5,6,8,9,10,11,12,13,15,16,18],smaller:[0,1,2,5,6,8,9,11,13,18],smallest:[0,4,14],smallest_row_index:14,smooth:[0,3,6,13],sn:[0,1,3,6,7],sne:11,so:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],soar:6,social:0,soft:[1,7,10,12],soften:8,softmax:[3,7],softwar:[0,8,15,16,17],sol:8,sole:[0,6],solid:[0,7],solitem:5,soltyp:5,solut:[0,1,2,3,5,6,8,10,11,13,16,18],solutionsummari:5,soluton:2,solv:[0,1,3,5,6,8,10,11,12,13,16,17],solve_expdec:2,solve_ode_deep_neural_network:2,solve_ode_neural_network:2,solve_pde_deep_neural_network:2,solveod:2,solveode_popul:2,solver:[2,5,7,8,9,10,11,16],some:[0,1,2,3,4,5,6,7,8,9,10,11,12,14,17,18],some_model:6,somehow:4,someth:[0,1,3,4,7,9,11,18],sometim:[0,1,11,12,13,14],soon:16,sophist:0,sopt:13,sort:[5,6,9,11,18],sound:[3,5],sourc:[0,1,3,6,15,16,18],space:[0,1,4,5,8,9,11,12,13,14,18],span:[0,3,5,9,11,16],spare:1,spars:[3,5,6,16],sparse_mtx:16,sparsecategoricalcrossentropi:3,sparsiti:10,spatial:[1,2,3,12],spdiag:5,speak:18,special:[6,7,10,12,13,16,18],specif:[0,1,2,3,4,5,6,7,8,9,11,12,15,16,18],specifi:[0,3,5,6,7,9,11,13,14,18],specifici:[0,10],spectacular:3,spectral:1,speech:[0,1,3,4,12],speed:[1,2,4,13],spend:18,sphere:0,spin:6,spite:0,spline:8,split:[1,3,4,5,6,8,9,10,11,14,18],splite:0,splitter:[1,10],spmatrix:5,spontan:18,spot:3,spread:[0,11,18],springer:20,spuriou:13,sqquar:5,sqrsignal:3,sqrt:[0,3,4,5,6,8,10,11,13,18],squar:[1,2,3,4,7,8,9,11,13,14,15,16,17,18],squarederror:10,squaredeuclidean:14,squash:12,squeez:2,srand:[],srtm:6,srtm_data_norway_1:6,sse4:[],stabil:5,stabl:[0,4,5,6,7,9,11,15],stack:[2,3,4],stacklevel:0,stage:[5,13],stai:[0,2,4,5,11],stand:[0,5,9,12],standard:[0,1,4,5,6,7,8,10,12,16,18],standard_basi:2,standardscal:[0,6,7,8,9,10,11],stanford:13,start:[0,1,2,3,4,5,6,8,9,10,11,12,13,14,16,17,18],start_box:[2,13],start_nod:[2,13],start_tim:14,startpoint:[],stat:6,state:[1,2,4,5,6,7,8,10,11,12,13,15,18],statement:[0,7,16],statist:[0,1,3,4,7,9,10,11,12,13,14,16,17,20],statu:[0,7,11],stavang:6,std:[0,4,6],stdev:[],stdout:5,steep:13,step:[0,1,2,3,4,6,7,9,10,11,12,13,14,16],step_fn:[7,12],step_length:13,steps_list:9,steps_per_epoch:[3,4],stereo:3,stian:19,still:[2,3,5,6,11,13,18],stimuli:12,stk2100:20,stk3155:17,stk4021:20,stk4051:20,stk4155:17,stk5000:20,stk:20,stochast:[0,1,5,6,8,11,12,17],stock:4,stoke:12,stone:[0,7],stop:[1,4,7,9,11,13,14],storag:5,store:[0,1,2,3,6,11,13,18],storehaug:19,str:[1,3,4],straight:[0,6,8,13],straightforward:[0,2,3,5,6,8,9,10,13,16],strategi:[0,1,9],stratifi:6,streamtyp:5,strength:[0,5,14],stretch:11,strict:[8,13],strictli:[8,13],stride:[4,16],strike:6,string:1,stroke:7,strong:[3,6,9,10,12,16,18],strongli:[0,8,15,16],stronli:0,structur:[0,1,2,3,6,9,10,12,15],stuck:[1,13],student:[0,17,19,20],studi:[0,3,4,5,6,7,8,11,12,13,15,20],studier:[17,20],style:[0,7,9,16],sub:[9,12],subarg:[2,13],subdivid:[0,16],subfield:0,subject:[5,6,8,18],subplot:[0,1,3,4,6,7,8,9,10,13,14],subplots_adjust:[8,18],subprogram:16,subract:0,subroutin:0,subscript:1,subsequ:[1,4,5,6,12,16,18],subset:[1,6,9,12,13,15],subspac:[0,8,11],substanti:[9,10],substep:11,substitut:[3,6,12,16],subsubset:9,subtask:6,subtl:1,subtract:[0,4,5,6,11,13,16,18],subtre:9,subval:[2,13],succeed:[0,4],success:[3,7,9,13,18],successfulli:[4,9],sucess:[],sudo:[0,15],suffer:[0,1,2,5,10],suffici:[1,6,8,11,13],suggest:[1,13,20],suit:[8,12],suitabl:[0,18],sum:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],sum_:[0,1,2,3,5,6,7,8,9,10,11,12,13,14,16,18],sum_i:[2,5,6,8,13],sum_j:6,sum_k:[6,8,12,16],sum_m:3,sum_n:3,sum_nx_:3,summar:[5,6,9],summari:[1,3,4,10,17],summat:3,sunni:9,superfici:3,superscript:[1,12],supervis:[0,5,6,7,9,12,15],supplement:7,support:[0,1,9,10,11,13,15,17],suppos:[0,5,6,7,8,10,11,12,13,16],suppress:[5,13],sure:[1,4,6],surf:6,surfac:[0,6],surpass:6,surpris:0,surround:[3,15],survei:[0,5,6],svc:[8,9,10],svd:[0,6,11,17],svdinv:5,svm:[8,9,10,11],svm_clf:[8,10],swap:2,swapax:2,swath:5,sy:[5,13],symbol:[1,5,11,13,15,18],symmeteri:1,symmetr:[0,5,8,11,12,13,16],symmetri:6,sympi:[0,15],synonim:18,syntax:[1,13],syntaxerror:1,syrk:5,system:[0,1,3,4,5,6,7,9,10,12,13,15,16,20],systemat:[4,6],t0:[3,6,13],t1:[2,13],t2:2,t3:2,t:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19],t_0:[2,9,13],t_1:13,t_:2,t_b:10,t_i:[1,2,12],t_j:[5,12],t_k:9,tabl:[9,18,19],tabul:0,tackl:4,tag:[2,3,4,5,6,7,12,13,14,16,18],taht:0,tail:18,tailor:[2,8,11],taiwan:0,take:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,18],taken:[0,1,3,6,10,13,16],tan:3,tangent:[1,4,12,13],tanh:[1,4,7,8,12,13],tape:[3,4],target:[0,1,3,4,5,6,7,8,9,10,11,12,13],target_nam:9,task:[0,1,3,5,6,9,11,12,14],tau:[3,5,18],tax:0,taylor:[2,13],taylornr:13,tc:8,team:1,teaser:0,technic:[0,5,6,13],techniqu:[0,1,8,10,13,15,17,18,20],technolog:[0,1],tek5040:20,tell:[0,4,6,10,11,13,18],temp1:1,temp2:1,temp:1,temperatur:[0,9],temporarili:1,ten:3,tend:[3,5,6,8,9,10,12,13,14],tendenc:0,tension:6,tensor:[3,4],tensorflow:[0,2,4,8,14,15,16,17,20],term1:[5,6,11],term2:[5,6,11],term3:[5,6,11],term4:[5,6,11],term:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,18],termin:[0,4,5,9,10,13],terrain1:6,terrain:6,test:[3,4,5,6,7,8,9,10,13,14,16,18],test_acc:3,test_accuraci:[1,3],test_error:6,test_imag:[3,4],test_ind:6,test_input:4,test_label:[3,4],test_loss:3,test_pr:1,test_predict:1,test_rnn:4,test_scor:[7,10],test_siz:[0,1,3,5,6,10],test_split:9,testerror:[0,6],testi:4,testpredict:4,testx:4,text:[0,1,2,4,5,8,9,11,13,16,17,18,20],textual:9,textur:1,tf:[1,3,4,13,14],tfe_py_execut:[3,4],th:[0,1,2,5,6,7,9,12,13,14,16,18],than:[0,1,2,3,4,5,6,7,9,10,11,12,13,15,18],thank:[4,6],theano:[1,15],thei:[0,1,2,3,4,5,6,7,8,9,11,12,13,16,18],them:[0,1,3,4,6,8,9,10,11,12,13,16],theme:0,themselv:[0,18],thenc:6,theorem:[2,6,7],theoret:[0,4,10],theori:[0,1,3,8,9,12,13,15,17,20],thereaft:[0,5,6,11,12,16],therebi:[0,5,7,11],therefor:[0,1,2,3,4,6,7,8,11,13,18],therein:11,thereof:[0,6,13],theta:[1,4,13,18],theta_:[1,13],theta_i:1,theta_k:[],theta_linreg:13,theta_t:13,thi:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20],thing:[0,1,2,4,5,7,9,18],think:[0,1,3,4,6,9,12,13,14,18],third:[0,3,6,13],thirti:7,those:[3,5,6,8,9,10,11,16,17],though:[1,2,3,4,13,16,18],thought:[6,14,18],thousand:[0,1],thread:[3,4],three:[0,1,3,5,6,8,9,12,16,17,18,19],threshold:[1,3,9,10,11,12,13],through:[0,1,2,3,4,5,6,8,11,12,13,14,15,16,18],throughout:[0,4,5,14,15,16,18],thu:[0,1,2,5,6,7,8,10,11,12,13,19],thumb:[0,6],thursdai:17,tibshirani:[6,17,20],tick_param:6,ticker:[6,13,18],tif:6,tight_layout:[1,7],tightli:11,tild:[0,5,6,11,18],till:[0,4,7,8,9,10,12,16],time:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,17,18],timefunct:[],timeit:4,timer:4,tini:1,tip:3,titl:[0,1,2,3,4,6,7,8,9,10,13,18],tmp:13,tmp_log:[3,4],tn:[2,3],to_categor:[1,3,4],to_categorical_numpi:1,to_numer:[0,6],to_str:[],todai:3,togeth:[0,3,6,8,11],toi:[13,14],told:13,toler:[2,6,14],tolist:4,tomographi:12,too:[0,2,4,5,6,9,11,13,18,20],took:8,tool:[0,1,3,6,13,15],toolbox:8,top:[0,3,5,6,9,10,15],top_node_typ:2,top_trac:2,topic:[0,5,6,7,8,15,17,20],topolog:[1,3,12],toposort:2,torkjellsdatt:19,toss:[10,18],total:[0,1,2,3,4,6,7,8,10,11,12,13,14,16,18,19],total_loss:4,totalclustervari:14,totalscatt:14,totalvari:[],toward:[1,2,7,12,13],town:0,tp:4,tpng:9,tqdm:6,tr:0,trace:[2,13],trace_stack:[2,13],traceback:[0,2,3,4,5,6,7,8,9,10,13],traceback_util:[3,4],tracer:[2,13],track:[3,13,14,16],tract:0,tractabl:0,trade:[5,9],tradeoff:[0,5,17],tradit:[0,1,4,6],train:[2,3,5,6,8,9,10,11,12,13],train_accuraci:[0,1,3],train_dataset:4,train_end:[0,1],train_error:6,train_funct:[3,4],train_imag:[3,4],train_ind:6,train_label:[3,4],train_pr:1,train_siz:[0,1,3],train_step:4,train_test_split:[0,1,3,5,6,7,9,10,11],train_test_split_numpi:[0,1],trainabl:4,trainable_vari:4,trained_model:6,trainerror:0,traini:4,training_checkpoint:4,training_dataset:4,training_gradi:13,training_gradient_fun:13,training_loss:13,trainingerror:6,trainpredict:4,trainscor:4,trainx:4,trait:0,trajectori:4,tran:5,transfer:9,transform:[0,5,6,7,8,9,10,11,12,13,15,16],transit:[6,12],translat:[1,4,6,10],transpos:[1,5,11,16],travers:[0,5],treat:[0,1,3,6,12,13,18],tree:[0,1,6,15,17],tree_clf:[9,10],tree_clf_:9,tree_clf_sr:9,tree_reg1:9,tree_reg2:9,tree_reg:9,trend:18,trevor:20,tri:[2,3,4,9,13],triain:0,trial:[0,2,4,6,13,18],triangl:13,triangular:16,trick:[3,4,8,11,13,18],trickier:18,tridiagon:16,trillion:15,trivial:[0,1,5,11,18],troubl:[0,8,12],truck:3,true_beta:6,true_divid:1,true_fun:6,tucker:8,tumor:[7,9],tumour:7,tunabl:1,tune:[4,9,13,16],tup:[],tupl:[2,13],turn:[0,1,5,6,7,8,9,10,11,12,13,16,18],tutori:[1,4],tv:2,tveito:2,tweak:[1,4,10,18],twice:13,twist:11,twister:[],two:[0,1,2,4,5,6,7,9,10,11,12,13,16,17,18,20],tx:13,tx_1:13,txt:4,ty:13,type:[0,1,3,6,8,10,13,16,18],typeerror:[2,13],typic:[0,1,2,3,4,5,7,9,10,12,13,18],u:[0,2,5,6,10,11,12,16],u_:16,u_i:12,u_m:10,ua:0,ubuntu:[0,15],uci:0,uio:[17,19,20],un:14,unari:16,unary_f:[2,13],unary_oper:[2,13],unary_to_nari:2,unbalanc:[6,9],unbias:[0,5,6],uncent:6,uncertainti:[0,5],uncertitud:18,unchang:[1,3],uncorrel:[10,18],undefin:5,under:[0,1,5,6,10,13,15],underdetermin:0,underfit:[1,6],underflowproblem:5,undergo:5,undergradu:17,underli:[0,1,9,13,18],underset:[4,14],understand:[0,1,3,5,6,10,13,14,15],understood:[8,13],undesir:8,undetermin:[5,8],undo:4,unexpect:6,unexpected:18,unfair:6,unfortun:[1,8,9,10],unicode_liter:[8,9],uniform:[0,1,5,6,11,13,18],uniform_real_distribut:[],uniformli:[13,18],unifrompdf:18,unimport:13,union:[5,6],uniqu:[0,2,6,13,14,16],unique_cluster_label:14,unit:[0,1,3,4,5,10,12,18],unitari:[5,6,16],unitarili:16,uniti:18,univari:18,univers:[0,1,2,13,15,17,19],unix:1,unknow:[0,16],unknown:[0,1,3,4,5,6,8,10,16],unknowwn:12,unlabel:1,unless:[0,3,6,11,13],unlik:[1,3,8,13],unnecessarili:9,unord:3,unravel:1,unrol:[3,11],unseen:[0,7,9],unstabl:1,unsupervis:[0,1,4,12,15,17],unsymmetr:16,until:[1,2,4,9,12,13,14],untouch:0,unusu:12,up:[1,3,4,5,6,8,10,11,13,14,15,16,17,18],updat:[1,2,10,12,13,14],upload:[15,20],upon:[1,6,11,16],upper:[0,8,9,16],uppercas:16,upsampl:4,upscal:4,us:[4,5,6,8,9,10,11,12,14,16,17,18,20],usag:[0,8,15],usd10000:0,usd:0,use_bia:4,use_multiprocess:[3,4],usecol:0,useless:1,user:[0,1,2,3,4,6,7,8,11,14,15,16],userwarn:[3,4,6,14],usetex:18,usg:6,usr:18,usual:[0,3,4,7,12,13,14],ut:5,util:[0,1,3,4,6,7,10,14],ux:16,v0:18,v1:18,v2:18,v:[2,4,5,6,11,13,15],v_0:11,va:1,val:13,val_accuraci:3,val_loss:4,vale:2,valid:[0,1,4,7,9,10,13,15,17,18],validation_batch_s:[3,4],validation_data:[3,4],validation_freq:[3,4],validation_split:[3,4],validation_step:[3,4],valu:[0,1,2,3,4,6,7,8,9,10,12,13,14,15,16,17],valuat:9,valueerror:[0,2],valy:4,van:0,vandenbergh:[8,13],vandermond:0,vanilla:[0,6,11,14],vanish:[1,4,13,18],var_x:18,varabl:8,varepsilon:[5,6],varepsilon_:[5,6],varepsilon_i:[5,6],vari:[0,1,3,5,6,10],variabl:[0,1,2,3,4,5,6,7,8,10,11,12,13,14,16],varianc:[0,1,5,7,9,10,11,13,14,15,16,17,18],variance_i:[5,11],variance_x:[5,11],variant:[0,1,6,8,12,13],variat:[3,4,11],varieti:[0,3,12,15],variou:[1,3,5,6,7,8,9,11,12,13,15,16,18],vartempvec:[],varvec:[],varydimens:4,vastli:3,vaue:1,vault:0,vdot:[2,13],vec:6,vector:[0,1,2,3,4,5,6,7,9,10,11,13,14,15,17],vector_mean:14,ventur:[0,8,15],verbos:[1,3,4],veri:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,18,20],verifi:[3,11,16],versatil:8,versicolor:[8,9],version:[0,3,4,10,13,14,15,16,18],versu:1,vert:[0,1,5,6,7,8,9,11,13],vert_1:[5,6],vert_2:[5,6,11],via:[0,5,6,7,8,9,10,11,12,15,16,17,18],vidal:11,video:[0,1,12,15,17],view:[1,3,5,6,12,13,17,18,20],violat:8,virginica:9,viridi:[0,1,2,3],virtual:1,viscos:13,viscou:13,visibledeprecationwarn:2,vision:[0,3],visual:[0,3,11,12,15],visualis:1,viz:[6,8,18],vjp:[2,13],vjp_0:[],vjp_0_fun:[],vjp_1:[],vjp_1_fun:[],vjp_argnum:[],vjpfun:2,vjpmaker:[],vjpnode:[2,13],vmax:[1,6],vmc:[],vmin:[1,6],voic:3,volum:[0,3],vote:10,voting_clf:10,votingclassifi:10,votingsimpl:10,vrtx:17,vs:[0,4,6],vspace:[2,13],vstack:[5,11,16,18],vt:5,w1:8,w2:[8,11],w3:8,w:[0,1,2,3,4,5,6,7,8,10,11,12,13,14,16,18],w_1:[8,16],w_1x_1:8,w_1x_:8,w_2:[8,16],w_2x_2:8,w_2x_:8,w_3:16,w_4:16,w_:[1,12],w_hidden:2,w_i:[1,2,10],w_ix_i:12,w_j:16,w_m:16,w_output:2,w_px_:8,w_px_p:8,wa:[0,1,3,4,5,6,7,10,11,12,13,14,16],wai:[0,1,2,3,4,5,6,7,8,10,11,12,13,14,16,18],walk:9,walker:18,wang:0,want:[0,1,2,3,4,5,6,8,9,10,11,12,13,14,15,18],warn:[1,3,4,8,14],warrant:6,wast:3,watch:[3,4,15],wave:3,wavelet:8,we:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20],weak:[9,10,14],weather:[1,12],web:[15,17],webpag:17,websit:[6,16,17],wedg:[8,18],wednesdai:17,wee:11,week:[5,6,7],weekli:[15,20],weight:[0,1,2,3,6,7,9,10,12,13,18],weigth:2,welcom:[8,15],well:[0,1,2,3,4,5,6,7,8,9,10,12,13,15,16,17,18,20],went:8,were:[0,1,3,4,5,6,7,8,10,11,12,14,18],wessel:0,westbi:19,westby:19,what:[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18],whatev:3,when:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],whenev:[13,18],where:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19],wherea:[6,18],wherein:[1,12],whether:[0,3,5,7,9,18],which:[0,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19],whichev:[1,3],white:9,who:[0,17],whole:[1,3,4,5,9,11,13],whose:[0,6,10,18],whow:[5,11],why:[0,1,3,6,13],wide:[0,1,3,6,7,12,15,16],widehat:6,width:[0,3,8,9],wieringen:0,win:10,wind:9,wing:19,winther:2,wiothout:6,wiscons:7,wisconsin:10,wisdom:6,wise:[0,1,5,12,13],wish:[0,2,5,7,8,11,13,14,16],with_std:0,wither:6,within:[0,2,3,4,7,9,12,13,14,18,20],withinclust:14,without:[0,1,5,6,8,9,11,12,13],won:0,wonder:8,word:[0,1,3,4,5,6,14,18],work:[0,1,4,6,7,8,9,13,15,17,18],worker:[3,4],workshop:17,world:[0,8],worldwid:0,wors:[0,1,3,4,6],worst:[],worth:9,would:[0,1,3,5,6,7,8,9,10,11,12,13,16,18],wrap:[2,6,16,17],wrap_util:[2,13],wrapper:0,write:[0,1,2,3,5,6,7,8,12,13,16],written:[0,2,3,5,11,12,13,15,16,18],wrong:[1,8],wrongli:10,wrote:[5,11],wrt:[2,10,13],wth:10,www:[15,16,17,20],wx_1:8,x0:8,x1:[4,8,9,10,13],x1_exampl:8,x1d:8,x2:[8,9,10,13],x2d:[8,11],x2d_train:11,x2dsl:11,x3:8,x:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],x_0:[0,5,11,16],x_1:[0,2,5,6,7,8,9,10,11,13,16,18],x_2:[0,2,5,6,7,8,9,10,11,13,16,18],x_3:[8,16,18],x_4:16,x_:[0,2,3,5,6,8,10,11,13,14,16,18],x_center:11,x_data:1,x_data_ful:1,x_hidden:2,x_i:[0,1,2,5,6,7,8,9,10,11,12,13,14,16,18],x_input:2,x_ix_:0,x_iy_i:8,x_j:[0,2,8,9,12,18],x_jy_j:8,x_k:[12,14,16,18],x_l:18,x_m:[6,12,16,18],x_n:[0,2,3,6,8,11,12,13,16,18],x_new:[9,10],x_offset:6,x_output:2,x_p:[3,7,9],x_poli:9,x_poly10:9,x_pred:4,x_prev:2,x_reduc:11,x_scale:8,x_test:[0,1,3,5,6,7,9,10,11],x_test_own:6,x_test_scal:[0,6,7,9,10,11],x_tot:4,x_train:[0,1,3,4,5,6,7,9,10,11],x_train_mean:6,x_train_own:6,x_train_scal:[0,6,7,9,10,11],x_val:1,xarrai:15,xavier:1,xbnew:13,xcode:[0,15],xdclassiffierconfus:10,xdclassiffierroc:10,xg_clf:10,xgb:10,xgbclassifi:10,xgboost:9,xgboot:10,xgbregressor:10,xgparam:10,xgtree:10,xi:[8,13],xi_1:8,xi_:8,xi_i:8,xk:8,xlabel:[0,1,2,3,4,5,6,7,8,9,10,13,18],xlim:[6,10],xm:9,xmesh:13,xnew:[0,13],xp:18,xpanda:0,xpd:[5,11],xplot:0,xs:9,xscale:0,xsr:9,xt_x:13,xtest:6,xtick:[3,6,8,9],xtrain:6,xu:0,xx:[0,5,16],xy:[0,6,8,16],xytext:8,xz:16,y1:4,y2:4,y3:4,y:[0,1,3,4,5,6,7,8,9,10,11,12,13,14,16,18],y_0:[0,5,11,16],y_1:[0,5,8,9,11,13,16],y_1y_1:8,y_1y_1k:8,y_1y_2:8,y_1y_2k:8,y_1y_n:8,y_1y_nk:8,y_2:[0,5,8,9,11,16],y_2y_1:8,y_2y_1k:8,y_2y_2:8,y_2y_2k:8,y_3:[0,9,16],y_4:16,y_:[0,1,5,6,10,11,16],y_data:[0,1,5,6],y_data_ful:1,y_decis:8,y_fit:0,y_i:[0,1,5,6,7,8,9,10,11,12,13,16],y_if_:10,y_ix_:0,y_ix_i:[7,8,13],y_iy_jk:8,y_j:[6,8,12],y_k:12,y_m:16,y_model:[0,4,5,6],y_n:[8,13],y_ny_1:8,y_ny_1k:8,y_ny_2:8,y_ny_2k:8,y_ny_n:8,y_ny_nk:8,y_offset:6,y_plot:9,y_pred1:9,y_pred2:9,y_pred:[0,1,4,6,7,8,9,10],y_pred_rf:10,y_pred_tre:10,y_proba:[7,10],y_scaler:6,y_test:[0,1,3,4,5,6,7,9,10,11],y_test_onehot:1,y_test_predict:0,y_tot:4,y_train:[0,1,3,4,5,6,7,9,10,11],y_train_mean:6,y_train_onehot:1,y_train_predict:0,y_train_scal:6,y_val:1,ye:[3,6,7],year:[0,15],yet:[0,1,6,8,11,13],yhd5w:17,yi:13,yield:[0,2,5,6,8,10,12,13,14,16,18],yk:8,ylabel:[0,1,2,3,4,5,6,7,8,9,10,13,18],ylim:[3,6],ym:9,ymesh:13,yn:0,yo:[8,9,10],yoshua:[1,20],you:[0,1,2,3,4,5,6,8,9,10,11,13,15,16,18,20],young:0,your:[1,2,4,5,6,8,11,13,15,16],yourself:[11,13],youtu:17,youtub:15,ypred:6,ypredict2:13,ypredict:[0,13],ypredictlasso:5,ypredictol:5,ypredictown:6,ypredictownridg:6,ypredictridg:[5,6],ypredictskl:6,ys:9,ytest:6,ytick:[3,6,8,9],ytild:[0,6],ytildelasso:5,ytildenp:0,ytildeol:5,ytildeownridg:6,ytilderidg:[5,6],ytrain:6,yx:16,yy:16,yz:16,z:[0,1,2,3,4,5,6,7,8,9,11,12,13,16,18],z_0:16,z_1:16,z_2:16,z_:[1,2,12,16],z_c:1,z_h:1,z_hidden:2,z_i:[1,12],z_j:[1,12],z_k:12,z_m:1,z_mod:9,z_o:1,z_output:2,zaman:18,zaxi:6,zero:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],zeros_lik:4,zfill:4,zip:[2,4,6,13],zl:5,zm_h:0,zn:0,zone:0,zx:16,zy:16,zz:16},titles:["<span class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks"],titleterms:{"1":[],"10":17,"11":17,"12":17,"13":[],"14":17,"15":[],"16":17,"17":17,"18":17,"19":17,"2":[0,17],"20":[],"2021":[],"2022":19,"21":17,"22":17,"23":17,"24":17,"25":17,"26":17,"27":[],"28":17,"29":17,"3":17,"30":17,"31":17,"34":17,"35":17,"36":17,"37":17,"38":17,"39":17,"4":17,"40":17,"41":17,"4155":[],"42":17,"43":17,"44":17,"45":17,"46":17,"47":17,"5":17,"6":[],"7":17,"8":[],"9":17,"case":[8,10,18],"do":1,"final":12,"function":[0,1,6,7,8,10,11,12,13,18],"import":[5,16],"new":4,A:[0,1,4,8,9],And:13,Ising:6,The:[0,1,2,3,5,6,7,8,9,11,12,15],With:4,activ:[1,12],actual:[],ad:6,adaboost:10,adam:13,adapt:10,adjust:1,adversari:4,again:[3,9],aim:[8,9],algebra:16,algorithm:[9,10,11,12],algortithm:13,all:8,an:[0,4,10],analys:5,analysi:[0,5,6,11,15,18],ani:13,anoth:9,appli:15,approach:[0,8,14],approxim:12,architectur:1,arrai:16,assist:19,august:17,autocorrel:18,autograd:[2,13],automat:13,back:[1,11,12],background:15,bag:10,base:13,basic:[0,5,7,9,10,11,16],batch:1,bay:5,befor:11,better:8,bia:6,binari:1,binomi:[],bird:10,block:[],boost:10,bootstrap:[6,10],boston:0,breast:1,bring:12,build:[1,3,9],calcul:[],cancer:[1,7,9,11],cart:9,central:[13,15,18],chain:12,chang:10,chi:0,choos:1,cifar01:3,classic:11,classif:[1,9,10],classifi:8,clip:1,cluster:14,cnn:3,code:[0,1,2,5,9,11,12,14],collect:[1,3],compar:[2,10],complex:[0,6],complic:6,compon:11,comput:9,con:9,concept:18,conjug:13,continu:[],convex:[8,13],convolut:[3,12],correl:11,cost:[1,10],cours:[15,20],covari:[5,11,18],cross:6,cumul:[],data:[0,1,3,6,7,9,11,15,18],dataset:[1,3],decai:2,decis:[9,10],decomposit:[5,11,16],deep:[1,2],defin:1,definit:[],degre:0,demonstr:[],dens:0,deriv:[5,12],descent:[2,10,13],detail:3,develop:1,deviat:[],diagon:11,dice:[],differ:8,differenti:[2,13],diffus:2,dimension:[2,3,8],disadvantag:9,discret:18,disguis:[],distribut:[5,18],domain:18,down:1,dropout:1,element:[0,18],elimin:16,ensembl:10,entropi:9,environ:0,equat:[0,2,12],error:[0,10],euler:2,evalu:1,event:[],exampl:[0,1,2,3,4,6,7,8,9,10],exercis:[0,6],expect:18,experi:18,explor:0,exponenti:2,extrapol:4,extrem:10,ey:10,fall:19,famili:1,famou:16,featur:[9,16],feed:[1,12],fine:1,first:[4,12],fit:[0,10],forc:3,forest:10,forward:[1,2,12],fourier:3,frank:6,freedom:0,frequentist:0,from:[5,10,12],full:2,further:[3,5],fy:[],gan:4,gaussian:16,gd:13,gener:[4,9],geometr:11,gini:9,good:0,grade:19,gradient:[1,2,10,13],growth:2,ha:15,handl:16,hidden:2,hous:0,how:[],hyperparamet:1,hyperplan:8,i:1,id3:9,idea:11,implement:1,implic:5,improv:1,includ:13,increment:11,index:9,inform:19,input:2,instal:15,instructor:19,interpret:[5,11],introduc:11,introduct:[0,6,15,16],invers:[5,16],iter:10,its:[],jackknif:[],jungl:10,kera:[1,3],kernel:[8,11],lagrangian:8,lasso:[5,6],later:5,layer:[1,2,3,12],learn:[0,1,2,11,13,14,15],least:[5,6],level:10,librari:15,likelihood:7,limit:[1,13,18],linear:[0,8,13,16],link:[5,11,17,20],logist:[7,13],lu:16,machin:[0,8,13,15],main:18,make:[0,9,10],mani:[10,12],materi:17,math:5,mathemat:[3,5,8],matric:[5,16],matrix:[1,5,11,12,16],matter:0,mean:0,meet:[5,10,18],mercer:8,mersenn:[],method:[6,9,10,13],mlp:12,mnist:[3,4],model:[0,1,4,6,12],moment:[],momentum:13,moon:[8,9],more:[3,6,16],multilay:12,multipl:[1,3],multipli:8,name:[],network:[1,2,3,4,7,12],neural:[1,2,3,4,12],non:8,normal:[0,1],norwai:[],notat:12,novemb:17,now:[1,9],nuclear:0,nueral:7,number:[0,2,18],numer:[2,18],numpi:16,object:3,observ:[],obtain:11,octob:17,od:2,off:6,ol:[5,6,13],one:[2,12],oper:16,optim:[1,8,13,15],ordinari:[5,6],organ:0,oslo:20,other:[4,9,11,12,16],our:[0,4,5,11,13],outcom:15,output:2,overarch:[0,4,8,9],overview:10,own:[0,10,11],packag:16,part:[13,15],partial:2,pass:1,pca:11,pdf:18,perceptron:12,perform:[1,9],period:3,perspect:1,point:4,poisson:2,polynomi:3,popul:2,practic:13,pre:[1,3],predict:4,prerequisit:[3,15],princip:11,principl:3,pro:9,probabl:[5,18],problem:[1,2,13],procedur:9,process:[1,3],program:[2,13],project:6,prop:13,propag:[1,12],properti:[5,18],pseudo:[],python:[0,9,15,16],quick:8,ran0:[],random:[10,11,18],read:9,real:6,recip:[],recurr:[4,12],reduc:0,reduct:3,reformul:2,regress:[0,5,6,7,9,10,13],regular:1,relev:20,relu:1,remark:3,remind:[6,8],requir:[2,15],resampl:6,rescal:6,resourc:2,revisit:13,ridg:[5,6],rm:13,rng:[],rule:12,s:[8,10],sampl:11,schedul:17,schemat:9,scheme:2,scikit:[0,1,11],select:[],semest:19,septemb:17,set:[0,2,3,9,12],sgd:13,should:1,simpl:[0,4,9,13],singl:10,singular:[5,11],situat:[],soft:8,softmax:1,solv:2,solver:13,some:[13,16],specifi:2,split:0,squar:[0,5,6,10],standard:13,state:0,statist:[5,6,15,18],steepest:[10,13],stk3155:[],stochast:[13,18],superposit:3,supervis:1,support:8,svd:5,systemat:3,teach:[17,19],teacher:19,techniqu:[6,11],technolog:15,tensorflow:[1,3],test:[0,1],textbook:20,theorem:[5,8,11,12,18],theori:18,three:[],tip:13,togeth:12,top:1,toss:[],toward:11,trade:6,tradeoff:6,train:[0,1,4],transform:3,tree:[9,10],tune:1,two:[3,8,15],type:[2,4,12],uncorrel:[],uniform:[],univers:[12,20],unsupervis:14,up:[0,2,9,12],us:[0,1,2,3,7,13,15],valid:6,valu:[5,11,18],variabl:18,varianc:6,variou:0,vector:[8,12,16],view:[0,4,10],visual:[1,9],vs:3,wai:9,wave:2,week:17,weekli:17,what:0,which:1,why:[],wisconsin:7,write:[4,11],xgboost:10,your:[0,10]}})
\ No newline at end of file
+Search.setIndex({docnames:["chapter1","chapter10","chapter11","chapter12","chapter13","chapter2","chapter3","chapter4","chapter5","chapter6","chapter7","chapter8","chapter9","chapteroptimization","clustering","intro","linalg","schedule","statistics","teachers","textbooks"],envversion:{"sphinx.domains.c":2,"sphinx.domains.changeset":1,"sphinx.domains.citation":1,"sphinx.domains.cpp":4,"sphinx.domains.index":1,"sphinx.domains.javascript":2,"sphinx.domains.math":2,"sphinx.domains.python":3,"sphinx.domains.rst":2,"sphinx.domains.std":2,"sphinx.ext.intersphinx":1,sphinx:56},filenames:["chapter1.ipynb","chapter10.ipynb","chapter11.ipynb","chapter12.ipynb","chapter13.ipynb","chapter2.ipynb","chapter3.ipynb","chapter4.ipynb","chapter5.ipynb","chapter6.ipynb","chapter7.ipynb","chapter8.ipynb","chapter9.ipynb","chapteroptimization.ipynb","clustering.ipynb","intro.md","linalg.ipynb","schedule.md","statistics.ipynb","teachers.md","textbooks.md"],objects:{},objnames:{},objtypes:{},terms:{"0":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18,19],"00":[0,1,3,5,6,11,18],"000":[1,3],"0000":[],"00000":[],"000000":[5,11],"00000000e":5,"0000e":3,"0001":[1,3],"000148":[],"00019998":5,"00024087":5,"00028369228101198006":[],"00029012":5,"0003043256065368937":9,"00031535148309580783":6,"0003153514830958081":[],"0003153514830958126":[],"0003153514830958235":6,"00034944":5,"000417932":2,"00042089":5,"000464088":2,"00050694":5,"00058016":6,"000599":[],"00060705":[],"00060708":6,"00061058":5,"00062582":6,"00062595":[],"00066668":6,"00068734":6,"00068941":6,"00069103":[],"00073541":5,"00076495":6,"00076919":6,"00076925":[],"0007698473260556325":6,"0007698473260556334":[],"0007698473260556339":[],"0007698473260556344":6,"00079123":[],"00079130":6,"00079968":5,"00084705":[],"00084711":6,"00085883":6,"00085884":[],"00087697":6,"00088573":5,"00092645":6,"00092646":[],"00096314":5,"001":[1,2,8,13],"00100519":6,"0010479245355337968":[],"0010479245887943952":6,"00105081":6,"00106677":5,"00107405":6,"00111756":6,"0011526":6,"00115999":5,"0011828302640124":[],"00118504":6,"00118527":[],"001263":[],"00128479":5,"001323":6,"00137823":6,"00137835":[],"00139705":5,"00149311":6,"00149956":6,"00152117":6,"00154733":5,"00156379":[],"00156382":6,"00168251":5,"00174276":6,"00175331":6,"00186347":5,"001988":[],"00198806":[],"00200":8,"00202624":5,"00202756":6,"00217499":6,"00224413":5,"00228741":6,"0023548":6,"002381316302584885":[],"002381316302584886":6,"0023813163025848865":6,"00242999":6,"00243186":6,"0024401":5,"00249435":6,"002526":[],"00266858":2,"00270244":5,"00274989":6,"00275135":[],"00289724":6,"00293838":5,"002948":[],"0030828":[],"003083":[],"00310113":2,"00312361":6,"00315593":6,"003215318065760509":[],"0032153180657605125":6,"00323332":6,"0032542":5,"003301":6,"003395515459204093":6,"0033955154592040944":[],"00353823":5,"0036237":6,"0036367":6,"00369758":6,"003704":[],"003717":[],"003759":[],"003774":[],"003788":[],"0038335":6,"00390021":[],"003909404072811217":6,"003909404072811231":[],"00391839":5,"0039987":6,"004":5,"004091940707753948":6,"0040919407077539514":[],"00410387":6,"00410478":6,"004113634617443116":6,"0041136346174431284":[],"004113634617443131":6,"004113634617443135":[],"00411363461744314":6,"004113634617443141":[],"004155986345861364":6,"004155986345861374":[],"00415763":[],"00424909":2,"00424967":6,"00426027":5,"00433417":11,"00440346":6,"00443743":6,"00445655":11,"004579219539673834":6,"004579219539673836":[],"00458878":6,"004610275230656187":6,"004610275230656294":[],"00462287":6,"00471782":5,"00472199":6,"00472512":6,"0049544":6,"004999999999999984":[],"004999999999999994":[],"004999999999999996":0,"00512927":5,"00517114":6,"00526348":6,"0053018":6,"00554552":6,"00556826":6,"0056799":5,"00577441":[],"00579953":6,"00588657":6,"00607783":6,"006162":6,"00617499":5,"00630331":6,"00642221":6,"00660427":6,"00672607":6,"00673393":13,"00673407":6,"00676387":6,"0068011":6,"00683748":5,"00683964":6,"007012403613997257":[],"007024126888936694":[],"0070241268889382116":6,"00719176":6,"00727646693":0,"007315":[],"0074331":5,"00751823":[],"00759119":6,"00784393":6,"00803064":6,"0080866254785146":[],"00813803":6,"00817631":6,"00823002":5,"00827728":6,"00831018":6,"00834567":6,"00848904":6,"0086649156":0,"008675369724975977":5,"008675369724976501":[],"00894639":5,"00905423":6,"009163470508352211":[],"009163470508352218":5,"009164545680330616":6,"00917248":6,"00934499":6,"009450756365829578":[],"0096208":6,"00990475":5,"00992331":6,"00996754":6,"00it":6,"01":[0,1,2,5,6,9,11,13,18,20],"010018312644139205":6,"010018312644139347":[],"0100706":6,"010331721306655144":6,"01033172130665515":[],"010516485576646513":6,"010516485576652856":[],"01066519":6,"01076611":5,"0110":18,"0110407067093945":[],"01104071":[],"011076219011339788":[],"01107621901137465":6,"011225":2,"0113104":6,"01179792":6,"01191824":5,"01199624e":18,"012073649439965807":[],"012073649472576395":6,"01212754811385597":18,"01219292":6,"01223198":6,"01231917":6,"012633802944855959":[],"01290947":6,"01295356":5,"013121573975499602":[],"013121573975499604":[],"01312157406137079":6,"013121574061370796":6,"01318643":6,"01347916":6,"01348565":6,"01367553":6,"01397146":6,"01405935":6,"01416528":6,"014209325470380275":[],"01433809":5,"014436800088896274":6,"014436800088969727":[],"01449782":6,"01458337":6,"0146081":6,"01463049":6,"015072388895177088":[],"0150723888951771":6,"015072388895177109":[],"015072388895177239":6,"01509543":13,"01530715721129232":[],"01531845":6,"01538461538462":[],"01549377":6,"01558197":5,"016285782696017055":6,"016285782696075054":[],"01633913":6,"01640891":6,"01655318":6,"016587414993037307":[],"016587414993045405":6,"01691985":6,"01708781":6,"01708852":6,"01713366":6,"01724499":5,"01735584819559184":[],"017355848195591845":[],"017355848195593354":6,"01756972":[],"01799917e":18,"01817152":[],"018232":[],"01831036e":6,"01831130e":[],"01831200e":[],"01831207e":6,"01866537":6,"01869785e":18,"01873344":11,"01873869":5,"01898855":6,"01905883":6,"01908936":6,"019587":[],"01963611":6,"01969145":6,"019754165271682844":6,"01975416527179247":[],"01975848":6,"02":[0,4,6,7,12,18],"02024962":6,"02054837e":6,"02068067":6,"02073509":5,"0209518407597062":18,"02098261":6,"02123176":6,"021250026482402":11,"02159270458799264":[],"021592704587992645":[],"021592704588021164":6,"021592704588021167":6,"02198702e":6,"022022882954618364":[],"02208512":6,"02228115":6,"02229529":6,"02252765":5,"022556":11,"02255619":11,"02299949826036602":[],"0229994982603662":6,"02348765":6,"02365049":6,"0245528":6,"02492265":5,"02493054":[],"02498832":6,"02503753":6,"02511518":6,"02522069":6,"025709":11,"02586427":6,"0260906":6,"02625193":8,"02625928":13,"026408391362671896":[],"02642055e":18,"026605727637176654":[],"02660572763718461":6,"026605727637184613":6,"02707227":5,"02723445":6,"027609773491022314":[],"027609773491022387":6,"027609773491022407":6,"02786488":11,"027865":11,"02857":4,"02917662":11,"029177":11,"02926396":[],"029613363972682966":[],"029733":[],"02976145":6,"02994311":5,"02f":6,"03":[1,6],"03032441e":6,"03056589":[],"030566":[],"03071040e":18,"03076923076924":[],"03077640549":4,"03099776":5,"031":5,"03187339273559067":[],"03196357":6,"03251863":5,"03256632e":1,"03267527":6,"03278964":[],"032790":[],"03279636":6,"0330308045180234":[],"0330308045183163":[],"0330308045183219":6,"0330308045190182":6,"033657685071527485":[],"033657685071527624":6,"03382304823545749":11,"03447512":6,"03562355":6,"03568439":6,"0358909447132981":[],"0359565":5,"03630548":6,"03707133":11,"037559":11,"03755944":11,"03761519":[],"03781367141738885":[],"03781367141738886":[],"037813671417388985":6,"03781367141738899":6,"03794112":16,"03814292":6,"03815288":6,"038300":11,"03850557":11,"038506":11,"039039":5,"03981057":16,"0399676689527968":6,"0399676689527975":6,"04":[1,6,11,18],"04010697":6,"04063602":6,"041":[],"041148729430502":6,"041148729430523":6,"0411487294305246":[],"0411487294305746":[],"04179719":[],"041797190646905":[],"04220758":6,"04284519":[],"043":[],"04315108":5,"0433816":[],"04346721":5,"04355837":6,"04362":[],"04368707":16,"0437499":2,"04389027":6,"043927769551648":[],"04423486":6,"044334":[],"0444119":[],"044613":6,"04483457":[],"04537385":6,"04543942":6,"04566964":6,"0458":9,"04615384615386":[],"04621521":[],"04648335":5,"0466":[],"04683565":5,"04775904":16,"04784395":6,"04818727730430296":[],"048187277304303056":6,"048653428302840175":16,"04892055":6,"04909093":6,"04912436":6,"04930820e":18,"0495569966278238":6,"0495569966278269":6,"0495569966278315":[],"04it":[],"05":[1,3,4,6,13],"05009826":6,"05087958":[],"05100875":6,"051649":11,"0517473":5,"05227921801205691":[],"05227921801205692":[],"052279218012057004":6,"05255759":[],"05263":[],"05290417684691035":[],"05302":[],"05364854":8,"05383795":6,"05434571":16,"05447415":6,"054585":[],"054785":[],"054963":[],"05505310046362":[],"05505310046363":2,"055137":[],"055320":[],"05533":[],"055676":[],"05614483":5,"05623":[],"05648":[],"05651951":6,"056528":11,"05667":[],"057088709963182":[],"057163681553428394":6,"05716368155351588":[],"0572":[],"05756733":[],"05785343":6,"05789007":6,"057899":11,"05796251":6,"05807125":6,"05883":[],"05884":[],"059013":5,"059294":11,"059378":11,"059601":5,"059783":[],"05999":[],"06":[6,13],"060080":[],"060183":[],"06020587":6,"06021285":[],"060213":[],"060228":11,"060309":5,"06043581":6,"060507":[],"060655":[],"060781":[],"060841":[],"061025":[],"061048":[],"061149":5,"061321":[],"061390":[],"061509":[],"061604":[],"06160438":[],"061701":[],"061745":[],"061898":[],"061917":[],"061923":11,"06200174":5,"06208238634231944":6,"06208238634231953":[],"062136":5,"062142":[],"062292565":4,"062294":[],"062391":[],"062409":11,"062411":11,"062435":[],"062451":[],"062534":[],"062676":11,"062706":11,"062834":[],"062884":[],"063052":5,"063078":[],"063250":[],"063343":[],"063364":5,"063513":[],"063631":5,"063657":[],"063752":11,"063851":5,"063872":[],"063874":5,"06388888888888888":[],"064052":[],"064108":[],"064110":5,"064184":[],"064274":5,"064384":[],"06444":[],"064469":[],"06453579006728315":[],"06453579006728322":6,"064538":11,"064604":11,"064648":[],"064658":5,"064681":[],"064838":5,"06491736":6,"064918":[],"064931":11,"064964":[],"06497046":[],"065020":5,"065106":[],"065249":11,"065314":11,"065348":5,"06547790180152352":6,"06547790180152353":6,"06547790180152357":[],"06547790180152363":[],"065514":[],"065557":[],"065575":[],"065641":11,"065730":11,"065826":5,"065892":[],"065974":5,"06602663":[],"066110":[],"066194":[],"066309":[],"06637":[],"066449":5,"066558":5,"066609":[],"066670":11,"0666807":2,"066729":11,"066753":[],"066789":5,"066837":11,"066954":[],"067079":[],"067213":5,"067236":[],"067240":5,"06724062":5,"067282":11,"067392":11,"067402":11,"067432":11,"067544":[],"067597":5,"067599":5,"067612":5,"067620":5,"067622":5,"067675":[],"067714":[],"067723":5,"067769":5,"067956":11,"068059":11,"068093":11,"068094":[],"068103":[],"068120":[],"068318":5,"068363":5,"068376":5,"06844519414009438":[],"06844519414009442":6,"06844519414009444":6,"068461":11,"068538":11,"06858699":[],"068608":5,"068642":5,"068659":[],"068735":5,"068806":5,"068809":[],"068831":[],"068937":[],"068965":5,"068991":5,"068992":5,"069085":11,"069091":[],"069181":5,"069275":11,"069408":5,"069409":5,"069441":5,"069455":[],"069584":[],"069672":[],"069685":11,"069754":[],"069796":5,"069847":5,"069877":11,"069884":11,"069888":5,"069912":[],"06it":6,"07":6,"070028":[],"070043":[],"070071":[],"070074":5,"070150":5,"07016":[],"070163":11,"07017":[],"070343":11,"070379":[],"07039":[],"070419":11,"070429":11,"070491":5,"070492":11,"070576":[],"070612":[],"07062318":6,"070630":5,"070635":[],"070889":11,"070911":[],"070950":11,"070972":11,"07099747918547346":[],"071022":11,"071086":[],"071112":5,"071136":5,"07115":[],"07129539":[],"0712953943627344":[],"0713":0,"071387":[],"07145103":11,"071467":5,"0714956":16,"071529":5,"07160048164232467":6,"07160048164248561":[],"071614":5,"071632":5,"071698":[],"071705":[],"071767":5,"071921":5,"071935":[],"07201957":[],"072216":11,"072242":[],"072348":[],"072442":11,"072492":[],"072530":[],"072617":[],"072620":[],"072750":[],"07285":3,"0728785":[],"07291729":18,"072928":[],"073016":[],"073062":11,"073136":11,"073225":[],"073230":11,"073280":11,"073362":[],"073368":[],"073423":[],"073436":11,"073449":5,"07345504":[],"073531":[],"073575":11,"073583":[5,11],"073598":11,"073629":11,"073695":[],"073699":5,"073719":5,"073761":11,"073810":11,"073816":5,"073827":11,"073907":[],"073949":5,"074034":5,"074067":11,"074111":11,"07413172":[],"074149":11,"07421084":5,"074237":5,"074336":[],"074375":5,"074488":[],"074530":11,"07456491":5,"074573":11,"074666":[],"074693":5,"07490892":6,"074945":[],"075195":5,"075241":5,"075573":[],"075594":5,"075760":5,"075820":[],"075959":5,"076130":11,"076204":[],"076216":11,"076338":5,"0764924":6,"07656896":[],"076589":5,"076604":11,"076628":5,"07678":[],"076780":11,"076804":11,"076895":11,"076905":11,"076938":11,"076955":5,"077003":5,"077013":11,"077015":11,"077022":[],"077083":11,"077211":11,"077349":11,"07735703":16,"077402":5,"077425":5,"077452":11,"077467":[],"07777777777777778":1,"077793":5,"078040":5,"0782":6,"07820":[],"078253":[],"078254":11,"078269":11,"078299":11,"078314":11,"078436":11,"078548":[],"07864":[],"078667":[],"07871":[],"07878641":16,"078911":[],"078992":[],"07903849":16,"079157":5,"079281":5,"079347":[],"079383":5,"079389":5,"079405":5,"079406":[],"07944154":16,"07968918676726029":[],"0796891867672603":6,"079729":[],"079839":[],"079847":[],"079893":[],"07989327":[],"07999999999998":[],"08":[13,18],"080106":[],"080137":[],"080256":[],"080297":5,"080312":[],"080319":5,"080334":[],"080347":[],"080370":[],"080406":[],"080410":[],"08041015":[],"08043851":5,"080517":[],"080582":11,"080593":11,"080647":11,"080706":11,"080738":11,"080801":[],"080847":5,"08085812":[],"080916":[],"080984":11,"081046":11,"081072":11,"081140":[],"081144":11,"081233":5,"081253":5,"08131003":6,"081388":11,"081431":[],"081483":[],"081533":5,"081540":[],"08156108":6,"081773":[],"082174":[],"082183":[],"082295":11,"0823185":[],"082451":[],"082503":11,"08251519":6,"082536":11,"082896":11,"082900":[],"08299273e":6,"083015":5,"083053":[],"08318298e":1,"083220":[],"083227":[],"083276":11,"08328216846752691":11,"08333333333333333":1,"08336233266":4,"083404":[],"083441":[],"083506":[],"083527":[],"083600":[],"083692":[],"08376632":6,"083766322923899":6,"0837663229239016":[],"0837663229239025":[],"0837663229239043":6,"083799":[],"083829":[],"083977":[],"084000":[],"084101":[],"084184":[],"084224":[],"084226":[],"08426840630693411":[],"08426840630693412":6,"08426840630693413":6,"08428156":16,"084282":[],"084364":[],"084400":[],"084408":[],"084414":5,"084444":[],"084484":11,"084536":[],"084549":[],"08455":[],"084570":[],"084604":[],"084629":[],"08464758160254343":5,"084670":[],"084683":[],"084702":5,"08474":[],"084777":[],"084862":[5,11],"084904":5,"084909":5,"084965":[],"085018":5,"085044":[],"085163":11,"085167":[],"085184":[],"085185":[],"085361":[],"085416":[],"08551306":6,"085764":[],"08576932":6,"085879":11,"08593216":6,"086074":11,"086076":5,"08611111111111111":1,"086112":11,"086303":[],"08630331":[],"086567":[],"08673755293381497":[],"086807":5,"086872":5,"086890":11,"08690":[],"086956":11,"087501":[],"08758":[],"08759":[],"087834":[],"08816688":[],"088176":[],"0881981":5,"088212":5,"088526":11,"088611":[],"088631":[],"08871404":5,"088809":[],"088816":[],"088825":[],"088853":[],"08888888888888889":1,"088926":[],"08902":[],"089059":[],"08917679":5,"089177":5,"089212":[],"089329":[],"089425":[],"089501":11,"089614":[],"0896981":6,"08996":[],"09":1,"090028":[],"090229":[],"09085624":16,"090929":[],"091051":11,"091060":[],"09117221":[],"091224":[],"091236":[],"091340":[],"091349":[],"091363":[],"091414":[],"091416":[],"091426":[],"09149881":[],"091630":[],"09166666666666666":1,"091696":11,"0917":9,"09170751":[],"09172408":6,"091891":[],"092066":16,"09216046":[],"0923":[],"09251":[],"09297039":[],"093247":[],"0934597075922044":[],"0938":[],"094082198961999e":6,"0940821989624095e":[],"094082198966615e":6,"0940821989673748e":[],"09440475":18,"094404754965417":18,"09444444444444444":1,"094472507965532":11,"095510":[],"095702":[],"095871":[],"09609807":5,"096173":[],"096472":11,"096623":[],"09672929714683368":[],"097360":[],"09744":[],"09744272":6,"09780":[],"09791":[],"09849763":11,"098498":11,"09856879":16,"09861229":16,"098802859381565":16,"099":[],"09903804":8,"0991919894927399":6,"099191989493334":[],"09951287404314545":1,"0n":0,"0s":[3,4],"0x11da42d90":13,"0x11e031e50":13,"0x7fad10f9a280":[],"0x7fad20f69be0":[],"0x7fd098df3280":[],"0x7fd0a9063be0":[],"1":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,17,18,19,20],"10":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,16,18],"100":[0,1,2,3,4,5,6,7,8,9,10,11,13,14,16,18,19],"1000":[0,1,2,4,5,8,11,13,14,15,18],"10000":[2,5,6,10,11,18],"100000":8,"10001":10,"1001":18,"1002":18,"1003":18,"10030":[],"10044225464078282":[],"1005":18,"10077114273548984":6,"1009":18,"1011":18,"1013":18,"1013904243":18,"10141413e":6,"1015":18,"102":3,"1022233262115424":[],"10222333":[],"1023":18,"10230":[],"1023111":16,"1024":3,"1026":18,"1027":18,"1028":[],"103":1,"1030":18,"10302062":16,"10340":[],"1037":18,"10378326e":1,"1038":18,"10391807":6,"10398646080125035":[],"10398646080125036":6,"10398646080125037":6,"1040":18,"10405456":11,"10430":[],"104411":[],"10455924":[],"1047":18,"10505137":16,"10520":[],"10555555555555556":1,"1056":[],"10589577":5,"106":[],"106095":11,"10620135":16,"108":6,"109":[],"10913":6,"10931453":6,"10960":[],"1096767776832326":18,"10967678":18,"10th":9,"10x":0,"11":[0,2,3,4,5,6,7,8,9,10,11,12,13,16,18,20],"110":[],"1100":18,"1101":18,"11022302e":5,"11046771":16,"111":[1,7,12],"11100":[],"1122558214":[],"112383":11,"1124":[],"1125":3,"11297834":[],"1136":3,"11362930e":[],"11388888888888889":1,"1139":3,"11390":[],"114":3,"11402309":16,"114437":[],"11462415":5,"11482289e":6,"11499517":[],"11507992e":1,"11547777218875695":6,"11547777218875696":6,"11547777218940905":[],"11547777218940906":[],"115822":6,"11590":[],"11598862":18,"11598862273198":18,"11604844":[],"116048442683864":[],"11660":[],"11666666666666667":1,"11674401539815678":18,"117":8,"11744554e":6,"11780":[],"11790868":[],"117986":[],"118":2,"1182":[],"1183":[],"118318":[],"1184":4,"11840":[],"1185":[],"1186":[],"11890":[],"119":2,"1199":[],"119936":2,"119999999999976":[],"11it":6,"12":[0,1,2,3,4,5,6,8,9,11,12,13,16,18,20],"120":[2,3],"1203":[],"1203284":8,"1206":8,"12069773":16,"121":[2,8,9,10],"12182967":6,"12196674":[],"122":[2,8,9,10],"12222222222222222":1,"122439":[],"1224392":[],"12288563":5,"122886":5,"123":[],"12318726e":6,"12333649":6,"12345260617257443":9,"123459876":[],"123711":6,"12380":[],"124":0,"12400":[],"125":[],"1250":[],"125000":[],"12552073e":6,"126":[],"1261":[],"12618549":5,"1265":[],"127":4,"1270":[],"127043":[],"1271":6,"1277":6,"127773":[],"12777777777777777":1,"12788968":[],"12790":[],"128":[3,4,13],"128664":6,"12898627064868978":[],"12937662":[],"12945452":[],"1297":[],"1298":9,"12adb44b1c20":7,"12m":3,"13":[0,2,4,5,6,9,11,12,13,16,18],"130":[],"13003291":6,"13055555555555556":1,"131":[],"13117061":0,"1314":[],"132":[],"13220608e":6,"13229545716505003":16,"132360":[],"1323603":[],"1326":[],"13280":[],"133":7,"13333333333333333":[],"135":[],"13519106":[],"13535942":6,"136":[],"1360":[],"13609760e":18,"1361":[],"1362":[],"1363":[],"1364":[],"13646574":5,"13661243e":6,"13679863":6,"1371":6,"13740":[],"137400784702911":[],"137652":11,"1378":[],"1382":[3,4],"1383":[3,4],"1384":[3,4],"1385":[3,4],"1386":[3,4],"13865173":5,"13876586436927824":5,"138775":11,"1388888888888889":1,"13890":[],"1392559581788775e":[],"1392559584983597e":[],"1392559585048734e":6,"13925595925919e":6,"14":[0,2,4,5,6,8,9,10,11,12,13,16,18,20],"140":[],"14021063":6,"1404":[],"14042769":[],"140428":[],"141":2,"14100":[],"1416398":6,"14174745":6,"1418":[],"142":2,"14250":[],"142857":[],"143":[2,7],"1435666":[],"14360598":[],"1437":1,"144":2,"14400":[],"1440501043841336":1,"14440":[],"1446729567":4,"14484695":16,"145":2,"146":[],"147":[],"14710":[],"14722222222222223":1,"147400":11,"14741468":[],"147420":11,"14783702":[],"1479":[],"148":[3,4],"14812206":6,"14818":[],"1484256":[],"148564":6,"14859":6,"148768":[],"149":[3,4],"149213":6,"14932651":[],"14988578":[],"149886":[],"14g":6,"14it":6,"15":[0,2,4,6,7,8,9,12,13,16,18],"150":[3,4,8],"15005476":5,"15098090e":6,"151":[3,4],"15130074e":6,"151986":[],"152":[3,4],"15200":[],"15258907":[],"1527777777777778":1,"153036":[],"153106":[],"1533795":[],"153760":[],"15384615384616":[],"154":[],"15454301":[],"154720":[],"15483121":[],"154911":[],"155":[],"155491":[],"155687":[],"155883":[],"156":[],"1562":[],"15629539":[],"15680777":16,"157":[],"15717291":16,"1575":[],"158":[],"1583767":16,"15843769515580663":18,"1586300629904382":[],"1587":[],"15891336":16,"159":[],"1590":[],"15990":[],"15g":6,"15it":[],"16":[1,2,3,4,5,6,8,9,10,13,18],"160":[],"1603":3,"1604":[],"1605":[],"160539":[],"1606":[],"1607":[],"16111111111111112":1,"1612":[],"16211139":5,"16220":[],"16231451":4,"1625":[],"1628":[],"163":[],"1630775253":[],"16342407":5,"16343471":6,"16384":3,"16500":[],"166":[],"16660817":[],"166667":[],"167":[],"16740002":16,"168":[],"16805821e":6,"16807":[],"16827044":[],"16832385":[],"16b8e3cda33a":1,"17":[1,2,4,5,6,8,13,18],"17084902":[],"1709":[],"17174962e":1,"17222222222222222":1,"1726":[],"17300":[],"1731":[],"17339342":[],"17446471":6,"1752":[],"175300":[],"17641709":6,"176880142835407":[],"17777777777777778":1,"17801022":5,"17861098":6,"17917768":5,"17930649":16,"17949575":5,"17953942":11,"1797":[1,3],"18":[2,4,6,7,8,9,10,13,18],"180":[],"18029127":5,"1807":4,"1809":[],"181":[],"1810":4,"1812":[],"18156717":16,"1821":[],"18276924":[],"18327677":[],"18333333333333332":1,"18393678":[],"184":[],"184519":[],"184895":[],"185":[],"1851":[3,4],"1852":[3,4],"18526":[],"185278747229417":[],"1853":[3,4],"1854":[3,4],"1855":[3,4],"186":[],"1860":[],"1861":[],"18611111111111112":1,"18613217e":6,"18660":[],"18673098":11,"18682538":[],"1887":6,"18912963":16,"189496":[],"189622":[],"18it":[],"19":[2,4,6,13,18],"19003":6,"191262820314401":[],"19166666666666668":1,"19207979":5,"19213479":[],"19220":[],"193":[],"19354258":[],"19379506":[],"19394283":[],"194":[],"1940":0,"19404282648955e":6,"194042826653172e":[],"194042826815498e":[],"1940428268204826e":6,"1943":12,"1944":[],"19444444444444445":[],"19466812":[],"1950915150":[],"1954":[],"1956":[],"19569961":6,"1961":[],"1962":[],"1963":[],"1964":[],"1965":[],"1970":16,"1973":9,"197370":11,"19740":[],"1979":6,"19800":[],"19853775e":18,"1989":[],"199":[],"19937":[],"19994371":6,"1_1":12,"1_2":12,"1_3":12,"1cm":[0,8,10,18],"1d":[1,2,3],"1e":[1,2,3,4,14],"1e10":14,"1e4":6,"1f":1,"1k":16,"1n":0,"1s":3,"1x":0,"2":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,20],"20":[0,1,2,4,6,7,8,13,18],"200":[0,2,3,4,8,9,10],"2000":0,"200000":[],"20015436":6,"2004":13,"2006":20,"2010":1,"2011":1,"2014":4,"2015":1,"2016":0,"20174121":16,"2018":[0,6],"2018906331":[],"2019":[],"2021":[6,14],"2022":17,"2027":[],"20277777777777778":1,"203":6,"20320502":[],"20375361":[],"203753611274545":[],"20500":[],"20509391":[],"206":[],"2060":[],"20661216":[],"2069":[],"207545":11,"20756638":[],"20833333333333334":1,"20867052175006306":6,"20867052175109335":[],"20980":[],"21":[0,1,2,4,5,6,7,9,12,13,16,18],"210340":11,"21058097":5,"21130":[],"2116753732":4,"21169159e":6,"21265216":[],"213":6,"213103":11,"213743":11,"21472683":16,"2147483647":[],"21519063":16,"21596432":6,"216290":11,"216683":11,"21786964":16,"2188":[],"21919813":[],"21944444444444444":[],"2195":3,"21985165":13,"21997099":16,"22":[0,1,2,4,5,6,12,13,16],"22044605e":5,"220506557673408":18,"22094791":[],"221":8,"221180":[],"2216":[],"2218":[],"221805":2,"222400":[],"2225":3,"225":4,"2259440937":[],"226124657522696":[],"22612466":[],"22690428":5,"2284246870217459":6,"22842468702288576":[],"22842468702288582":[],"22847924":5,"22929905e":18,"2299":3,"22it":6,"23":[1,2,4,6,7,12,13,16],"23002365e":6,"23014274e":18,"231":[],"23103285":[],"2315033":[],"23167717":5,"2321528":5,"232153":5,"232435":[],"23297056":[],"23333333333333334":1,"233528":5,"234":6,"23522201":[],"2357089093":[],"2360682191515046":[],"2361111111111111":[],"2364":[],"237038":5,"23703839":5,"2379":6,"2397":[],"24":[0,1,2,4,6,13,16],"24140":[],"2416":[],"24175744e":6,"2419":[],"24276315":16,"2430":[],"24390":[],"24444444444444444":1,"244858":[],"24485843":[],"24512498":16,"246":2,"2470":[],"24770094":6,"24829908":5,"24906604e":6,"24924624":[],"2495":[],"25":[2,3,4,5,6,7,8,9,11,13],"250":[2,4,7,9],"2500":[],"25000":0,"250000":[],"250154":[],"2517560119":[],"251879":[],"252436":[],"25259666":16,"2526":3,"252866":[],"25286618":[],"253775":[],"255":3,"255001":[],"256":[2,4],"25617654e":6,"25617657e":[],"25617658e":6,"256962":[],"257":[],"2572":[],"2572495066":[],"2572e3a4b38d":1,"2575":[],"25792767":16,"25845e8df859":6,"259153":11,"2597":[],"25976336":[],"26":[2,4,6,13],"26079358":[],"26153846153846":[],"26186844":16,"2619":[],"26290036":[],"26294938":[],"2629493813057833":[],"26301436":5,"26306244":[],"264":4,"26409315307910025":6,"2640931530791003":[],"2640931530791005":[],"26409315307910053":6,"265":[],"2650":[],"265109911":4,"26531223e":18,"2654":[],"266":[],"26610075":[],"26666667":13,"26710969":5,"26780278":5,"268":[],"268227":[],"26822717":[],"269":[],"27":[0,1,2,4,6,13],"270":[],"2703":3,"27092897":6,"2750":[],"275341":[],"27562809e":18,"27621662e":18,"276263":11,"27692307692308":[],"27700":[],"2772":3,"2775623201":[],"27760":[],"278036":5,"27803645":5,"27n_":18,"28":[1,3,4,6],"28001319":[],"28047021":16,"280647":11,"28067036":16,"28097861":5,"280979":5,"2812":[],"282727":11,"283":[],"2830637392":4,"28336218e":6,"2836":[],"28390":[],"28475098":8,"28590743":[],"2861":18,"28634473":11,"286345":11,"2871":[],"2873":9,"2882":18,"2886":18,"2886847885377843":[],"28868479":[],"28875373":16,"2890":0,"2892":18,"28967287":16,"29":[4,6,7],"29149329":[],"2915":18,"29167186":5,"29229741":13,"2923076923077":[],"29512284":11,"295123":11,"2954":[3,4],"2955":[3,4],"2956":[3,4],"2957":[3,4],"2958":[3,4],"29588901":13,"29592539":[],"296247":[],"297":[],"2971492148":[],"2972":[],"29822833":6,"298273":[],"298375":[],"299267190588216":11,"299748":[],"2_":12,"2_1":12,"2_2":12,"2_3":12,"2_i":12,"2_m":[6,18],"2_t":13,"2_x":18,"2b":18,"2cm":8,"2d":[1,3,11,12,15],"2e":6,"2f":[0,7,9,10,11,12],"2ff97f4bf03b":[],"2g":2,"2g_i":2,"2k":3,"2m":6,"2n":[0,2,3],"2nd":9,"2p":18,"2pt":4,"2s":4,"2x":[0,3,8,13],"2x_ix_jy_iy_j":8,"2x_j":8,"2y_i":10,"2y_j":8,"3":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19],"30":[0,1,4,6,7,10,13],"30000":0,"30010":[],"30119421":8,"3017":[],"3018":[],"3019":[],"301927":[],"3020":[],"3021":[],"30258509":16,"302m":4,"303":6,"3037":[],"3038":[],"30384239":16,"3039":[],"303m":4,"3040":[],"3041":[],"30466214e":6,"304m":4,"30535506":[],"30552077":16,"30559547":[],"305m":4,"3063":[],"306m":4,"3072":3,"30761405e":18,"30787294":6,"307m":4,"308m":4,"309":0,"30940":[],"30b53504a633":0,"31":[4,6,12,16,18],"310":0,"311":0,"311m":4,"312":0,"3120271598582915":18,"3123314713548606":6,"31276579e":6,"312m":4,"313":0,"31318084":5,"314":[],"31457796":5,"315":6,"3155":[5,6],"316":[],"31605061":16,"31614188e":18,"317":[],"31718909":11,"317367":11,"317m":4,"31866499":13,"31896852":8,"319m":4,"32":[3,4,6,12,13,16,18],"3200":1,"32039227e":18,"32047562":[],"3208":2,"320m":4,"32149601703519115":6,"3214960170351912":6,"3215":[],"32179365":[],"32309075":[],"32320052":16,"324":2,"32458459":[],"324m":4,"3250":[1,6],"32512":[],"326238":[],"326m":4,"32721178":11,"327212":11,"32750531e":18,"32816737":[],"32903042":[],"3297":[],"32992274":[],"329923":[],"33":[3,4,12,14,16],"3303":[],"33066907e":5,"3310":[],"33166055e":5,"331939":[],"331m":4,"332331":[],"333":7,"3331":[],"33327369":[],"333274":[],"33333333":13,"3337":[],"33611111111111114":[],"33711888":[],"3384":[],"33900713":[],"33m":4,"34":[4,7,16],"3403":[],"34040204e":18,"340782":11,"34114547":5,"341m":4,"342680":[],"34305928":[],"3436":0,"3437":0,"3456":[],"34568872":16,"34569596":5,"3457":[],"3458":[],"34585355":16,"3459":[],"3460":[],"346810":[],"3469819128513336":[],"34740615":[],"3498":[],"34it":6,"35":[2,4,6,7],"3514":[],"35140":[],"35147135":16,"351636":11,"35182854":5,"35203688":16,"3522":[],"3525":[],"3528556":[],"352856":[],"3529":[],"3530606977":[],"3538":[],"35396404e":18,"35401056e":18,"3543":[],"3544313922":[],"3546":[],"35470445e":5,"3548":[],"355":[],"3551":[],"35533773":6,"35533774":[],"3556":[],"35562617e":18,"3558":[],"3560":[],"356399":[],"3567":[],"3572":[],"35724288e":18,"3575":[],"357508":[],"3576":[],"3577":[],"35771842":6,"3581341341":4,"3585":[],"3587":[],"35892474":16,"359":5,"3593":[],"3594821":13,"3595":[],"3598":[],"359999999999985":[],"36":[0,2,4,5,6,7,18],"360":1,"3604":[],"3605":[],"3606":[],"360688":[],"3609":[],"36102113":[],"36117602":[],"3613":[],"3615":[],"361556":[],"3617":[],"3621311":5,"3624":[],"3627":[],"3628":[],"363295916323784e":6,"363295916414895e":[],"3632959170548605e":[],"363295924430451e":6,"363834":11,"36383443":11,"3643":[],"364418e97433":1,"3645":[],"3646":[],"3647":[],"3655":[],"3655222":5,"3659":[],"3668":[],"36689784":[],"366898":[],"3669":[],"367":2,"3672":[],"3673":[],"3674":[],"3679":[],"3684":[],"3687":[],"3688":[],"368m":4,"369139":11,"36it":[],"37":[2,4,6,7],"3704":[],"370782966":4,"3711":[],"3713":[],"3718":[],"3721":[],"3722":[],"3725":[],"3729":[],"37307168":16,"3739":[],"37396662":6,"37415316":16,"374291":5,"37429133":5,"3748":[],"3749":[],"3753":[],"3759":[],"3760":[],"3765":[],"376559":[],"37655936":[],"37692363":[],"37703055":[],"3772":[],"3773":[],"37732":[],"3776":[],"3777801602":[],"3779":[],"37835429e":[],"3784":[],"37900111":6,"3791":[],"3794":[],"379647":11,"37964744":11,"37992857":[],"38":[2,4,7,18],"380":[],"3802":[],"3803":[],"3804":[],"3805":[],"380739":5,"38073947":5,"3811":[],"38135733e":6,"3815":4,"381627865854956":[],"38162787":[],"3817475779":[],"3818":[],"3819":[],"3820":[],"38246359":[],"3827":[],"382951":11,"38295101":11,"3830":4,"3833":[],"38336316":16,"3834":4,"3837":4,"3838":4,"3839":4,"3842":[],"3850":[],"3851":4,"38533184":6,"3854":[],"3858":4,"386":[],"3861":4,"3862":4,"38629436":16,"3864":4,"3867":[],"3869":4,"387":[],"3871":4,"3872":4,"387482":5,"38748219":5,"3876":[],"388":[],"3881":4,"3882":[],"3885":[],"38868469":16,"3888":4,"3889":4,"389":[],"38901478":[],"3891":4,"38916861e":6,"38962192e":6,"3898":[],"39":[0,2,4,13,19],"390":[],"3906":4,"39095416":[],"3914":4,"3915":4,"3916":4,"391602":[],"3917":4,"3922":4,"3925515884752442":[],"3928":4,"3931":[],"39311435":16,"3943":4,"3944":4,"39456996":16,"3950":4,"3955":4,"39560937":16,"39579407":5,"3958":[],"3960":4,"3962":4,"3970":4,"39706038":5,"39724390e":18,"3975":4,"397700":11,"39789527":16,"3979":4,"39794864e":18,"3980313467":[],"3983":4,"3994":4,"3996":4,"399836":[],"3d":[2,3,4,6,13],"3f":[1,3,9],"3n":16,"3x":[2,8],"3x_i":2,"3y":8,"4":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],"40":[1,4,6,19],"400":4,"4000":20,"4005":4,"4011":4,"40116777":[],"401168":[],"4012":4,"4017":4,"401842":11,"40214433":16,"4022":[],"40236901e":18,"4027":4,"4030":4,"40340043":[],"4034668048":[],"4038":4,"4039":4,"404":[],"4045":4,"4048":4,"4055":4,"405890":11,"4059":4,"40599799":[],"4068":4,"4069":4,"4075":4,"4077":[],"408":2,"4082":[4,6],"4084":4,"4087":[],"4088":4,"4089":4,"409":2,"40927184e":6,"40a38ad763f1":6,"41":[2,4,16],"410":2,"4100":4,"4107":[],"411":2,"4115":4,"4117":4,"412":2,"4128":4,"4131":4,"41357064":[],"4143":4,"41433969":5,"4144":4,"4146":4,"41511965e":1,"4155":2,"4162706317":[],"4167":4,"4176":4,"4177":4,"418506":11,"4186":4,"41876267":5,"418763":5,"41882036e":6,"41882037e":6,"4192423635":[],"41958102e":18,"4198":4,"41990268":[],"41it":6,"42":[1,2,4,8,9,10,16],"420":[],"42037468e":18,"42078103":[],"421":2,"4212":4,"422":2,"4224":4,"423":2,"42340403e":18,"42394972":16,"424":2,"42441033":5,"42449643":[],"42450":[],"425":2,"4255":[],"4258989918":[],"426":[6,7],"42615374e":18,"42631342":[],"427017":11,"4275":4,"43":[1,2,4,7,16],"43054282":5,"43135183":16,"43294696e":18,"43330971e":6,"434932":11,"43493232":11,"435163":[],"43552433":16,"43579948e":6,"436462435":4,"43761347":[],"43766686":11,"4379":4,"438060758":[],"438136":[],"439230":6,"44":[1,2,4,16],"44089210e":5,"4410":[],"4411":[],"442600":11,"443217":[],"444":[],"44688507e":18,"44970586e":1,"45":[2,4,19],"450":[],"450257":11,"4504":[],"45290829":16,"45393214e":18,"4543859":[],"4557763":11,"455947":[],"456":[],"456418966187335":[],"457":[2,4],"457770268480242":[],"458027":[],"458078":11,"45960079":5,"46":[2,4,19],"4601":[],"461":[],"461175":16,"462":7,"46383925e":6,"464424":[],"4644244":[],"46508305":16,"46567887":16,"466":[],"46602982":[],"46696223":[],"469":[],"46932688":[],"4694":[],"46984697e":6,"469868":[],"46986815":[],"47":[4,19],"4703":4,"47042744":5,"470714":[],"47075725":6,"47116868e":6,"47125748":5,"47132891":5,"47387858":[],"47400238":[],"47432993":[],"4744":[],"47478057":[],"47485224":[],"475311":[],"47531107":[],"47610036":6,"47815203":11,"47920156":[],"47942814":5,"479465113":4,"48":4,"48089797":[],"48133064":[],"481979":6,"48212873":16,"48257387":19,"48316523e":18,"483257001":[],"4837":[],"48420165":[],"48476997":11,"48574149":16,"486873":[],"48687342":[],"489":[],"48994188":5,"49":[4,5,6,11,13],"490":[],"491":[],"49152":3,"492":[],"493":[],"49385454e":18,"4940954":0,"4959161509356135e":6,"495916150936645e":6,"495916150936654e":[],"495916150938325e":[],"49614357":18,"49636583":[],"497":[3,4],"498":[3,4],"499":[3,4],"4990":18,"4992":18,"4997":18,"49992743e":18,"4c4c7f":[9,10],"4d":3,"4f":6,"4y":8,"4y_i":10,"5":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],"50":[1,2,3,4,6,7,8,10,13],"500":[1,3,4,6,9,10,13],"500000":[],"50000455":5,"50000553":5,"50000718":5,"50000855":5,"50000969":5,"50001063":5,"50001142":5,"50001207":5,"50001261":5,"50001306":5,"50001343":5,"50001374":5,"500014":5,"50001414":5,"50001422":5,"50001439":5,"50001454":5,"50001466":5,"50001476":5,"50001485":5,"50001492":5,"50001498":5,"50001502":5,"50001506":5,"5000151":5,"50001512":5,"50001515":5,"50001517":5,"50001518":5,"50001519":5,"50001521":5,"50001522":5,"50001523":5,"50001524":5,"50001525":5,"501":[3,4],"50105159":[],"5018":18,"50227564e":6,"5031474174499113":18,"50314742":18,"50321091":5,"504167":[],"50416731":[],"506":0,"50680321e":18,"50727059":16,"50769230769231":[],"507d50":[9,10],"50846111e":6,"50846112e":6,"50it":6,"50j":13,"50x10":1,"51":[4,10],"510":1,"51004249":16,"51126895e":18,"512132":[],"51214899":6,"51265232":16,"51289697":11,"512897":11,"5138":[],"51389553":16,"514219":[],"51561271":16,"517350858882083":[],"5177783846":4,"518030":11,"51803019":11,"519842":[],"52":[3,4,13],"5222222222222223":1,"522758":[],"52307692307693":[],"5260627":[],"526744":11,"52723079":[],"52773051":[],"52799362":11,"528115":11,"52811546":11,"52874252":5,"529":[],"52911941":[],"52945798e":[],"53":[3,4,9],"5303329":11,"5305555555555556":1,"5312":[],"531280":[],"53189647":[],"53312754":16,"53423784":16,"53603432":[],"53611562":[],"53697476":[],"53703498":6,"5378811":11,"53794784":11,"537948":11,"53846153846155":[],"539261":11,"54":[3,4,6,18],"540":[],"54016188":11,"540162":11,"54039921":5,"54041041e":5,"541605":[],"54163136":13,"544439":[],"546166676":[],"54780216":[],"54it":[],"55":[1,3,4],"5501":[3,4,14],"550321":[],"5539":[],"55505907":16,"5555555555555556":1,"55578041":[],"5566":[],"557795":11,"55854694":11,"5594":6,"56":[1,3,4],"56033697":5,"561":[],"5611":[],"5615":[],"56198284":5,"5625":[],"56302854":16,"5639":[],"564":[],"564374":11,"565":[],"56536":0,"566":[],"56636616e":6,"567":[],"568":[],"569":1,"56912044e":6,"56939714":5,"56992937":[],"57":[0,4,8,19],"570":[],"5700":[],"571":5,"571105947979336e":6,"571105947979394e":6,"571105947979395e":[],"571105947979439e":[],"57154252":[],"57174058":[],"57201944e":6,"57266138":[],"57361898":[],"574465":11,"57673618":16,"57842073e":18,"57it":[],"58":[4,10,19],"583595":[],"58395707":[],"58428804":5,"584804":[],"5888888888888889":1,"589":[],"58986647":[],"59":[2,4],"590":[],"591":[],"591317992":4,"591594":5,"59159438":5,"592":[],"593":[],"5944444444444444":1,"59591979":[],"595920":[],"59592669":[],"59766":5,"597660":5,"59833875":13,"598392":[],"59839245":[],"59955801":13,"5cm":18,"5dd54edf2138":6,"5f":8,"5x":8,"5y":8,"6":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,16,17,18],"60":[1,2,3,4,6],"60000":4,"6019067271":4,"60236938":16,"60293962":5,"60420593":5,"60538875":16,"60619654":6,"6063219":[],"606439":[],"60673226":11,"60685864":6,"60791699":[],"6079169911277265":[],"60943791":16,"61":[2,4,7],"61033524":[],"6111111111111112":1,"612939":[],"613579":[],"61399851":11,"613999":11,"61463451":16,"614808":11,"61576236e":18,"618":[],"61808351":[],"618982":[],"6199381169260247":[],"62":[2,3,4],"62168613":[],"62364974":[],"62373464":11,"625":7,"626635268":[],"62767384":[],"62894215":5,"629961":[],"63":[0,1,2,3,4,6,7],"6300745149331701":[],"630224":[],"63025821e":6,"63207808":18,"63249532e":6,"63270833":[],"63395187":[],"63498144":5,"63675140":[],"6371293350711955":[],"63860687":16,"63901111":[],"63it":[],"64":[1,3,4,7,13,16],"64012627":5,"64113381e":18,"64141716e":18,"64293754":[],"64425009":[],"645":[],"64615384615385":[],"646283":11,"647":6,"64742912e":6,"647473":11,"649339":5,"64933923":5,"649382":11,"64x50":1,"65":[1,2,3,4,7,8,9],"65136857":[],"6530742540053943":[],"6562":[],"65626043":5,"65628853":16,"65885453":5,"659306":[],"66":[2,3,4],"66054752":[],"66087937":[],"66204648":6,"66226149":6,"66247212":[],"6628996975186953":[],"66292841":[],"66310422":[],"6638":[],"66410989":[],"66560":[],"66619972":11,"666200":11,"667":[],"668172":[],"668186":[],"66818635":[],"66it":6,"67":[2,4],"67047975e":6,"671089":[],"672721":[],"67314874e":5,"67588315":[],"67591616":[],"67697934":11,"67838309":[],"67882608":[],"68":[2,4],"68176047e":18,"68192193":5,"68246089":16,"68279358":16,"6834195":[],"68534263e":6,"68542204":5,"68545647e":[],"6869":[],"6887363571":4,"68929213e":6,"68944595":[],"689519":11,"69":[2,4,7,18],"690":[],"690569639355314":[],"69061276":11,"690617":[],"69069n_":18,"692":1,"69295955":[],"69484813e":5,"69493539":16,"69504801":6,"69634577e":6,"69695259":5,"69985355":[],"6999536":11,"69it":[],"6e75736fdab1":6,"6f7a6bd7d79f":6,"6m":[3,4],"6n_":18,"7":[0,1,2,3,4,5,6,7,8,9,11,12,13,16,18,20],"70":[1,2,4,6,7],"70127680":6,"70179437":[],"70205195":16,"70354373":[],"703716d317a7":8,"70434005":[],"7050":[],"70598996":[],"70653767":4,"70710678":5,"70769586":[],"70831425":[],"70832814":5,"70980493":16,"71":[1,2,4],"71131626":16,"7119":[],"712018":11,"7134":[],"71350226":[],"7151":[],"71721168":[],"718697":5,"71869727":5,"72":[2,4],"72174172":11,"722047011333792":5,"72271878e":6,"72347283":[],"7236674":5,"72373129":[],"724":3,"72522848":[],"72859758":5,"7293182":[],"72981762":8,"73":[4,6],"731000":[],"73174557":[],"73287103e":18,"733096":[],"73406033e":18,"73484667":[],"735738558766299":[],"73573856":[],"73752910":[],"73it":6,"74":[4,6,13],"740":[],"74081822":8,"74368436":16,"743u":[],"7448615806559786":[],"7469898175164704":[],"74840212":5,"749765":[],"75":[4,5,6,8,11],"7501749450963715":[],"750445":[],"750u":[],"75106135":16,"75118364":[],"751699":11,"75170092":5,"753846153846155":[],"75517445":[],"756352":[],"75838233":[],"75it":[],"76":[2,4,19],"76060096":[],"76066069":16,"763u":[],"7643536":13,"765":7,"76529528":[],"76674796":16,"76731400e":18,"76802186":[],"76923076923077":[],"76936315":5,"7694444444444445":1,"77":[2,4,13,19],"77034458":[],"770345":[],"77152076":5,"7718":9,"773329728649545":[],"77332973":[],"77333117e":18,"774300":[],"775":[],"77627886":13,"77632220e":18,"77636e":13,"77662945":[],"7767978193240488":11,"77714169":8,"77754132e":18,"7782028952":4,"77865169":[],"78":[2,4],"78011544":[],"78156479e":5,"78184120e":6,"7846153846154":[],"78941903":5,"78988962e":18,"79":[2,4],"79009329":16,"79093776":[],"79111643":5,"791123":[],"79145214e":18,"793167":[],"79394867":[],"7939646":[],"794282":11,"79449156":[],"79602861":[],"79754246":[],"797e":6,"79856831e":18,"7c394b1e8b71":9,"7d7d58":[9,10],"7f2b3a6174c2":13,"7m":4,"8":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,17,18],"80":[0,1,2,4,5,6,8],"800":[4,7],"80315282":5,"803153":5,"80354994":6,"80469739":5,"8055555555555556":1,"80561191":[],"80609615e":6,"80609616e":6,"8064":[],"80734875":[],"807349":[],"80738685":16,"80747253":16,"80842254":11,"808423":11,"80847477e":6,"81":[1,2,4],"81048318e":6,"8108619":[],"81160425":5,"81187794":16,"81333804":6,"81333805":[],"81388724e":18,"814":[7,11],"815563":[],"81633628":11,"816454":[],"81671821":[],"816847":[],"8175":[],"82":[2,4],"8219235992494145":[],"8219236":[],"82198978":5,"82650876":[],"826509":[],"8265786":5,"82747653":[],"827477":[],"82773778":[],"829648415811072":18,"82964842":18,"83":4,"8305555555555556":1,"83512277":5,"836874":5,"83687444":5,"83698677":16,"83999999999999":[],"84":4,"84062065":[],"84228957e":[],"84232163":16,"842436":[],"84292394":[],"842u":[],"84355903e":1,"84443254e":1,"84569271":13,"84780262":6,"84854738":[],"84923989e":6,"84924834":11,"84994524":5,"85":[1,4],"8503720991789538":5,"85091337":[],"85263220":6,"8527777777777777":[],"85278920e":5,"853241":[],"85324115":[],"853u":[],"85601992":5,"85758696":16,"858":[],"8583333333333333":1,"85953586":6,"85959007":[],"86":2,"8608479":[],"860848":[],"861":[],"86117291":5,"86134827":5,"86145244":11,"8638888888888889":1,"86478158":[],"86546962":[],"86623151":13,"86630":[],"8666666666666667":1,"86810":[],"869":0,"86905621":[],"86925797":5,"869258":5,"87":2,"870":0,"8702784034":4,"871":0,"872":0,"8722222222222222":1,"873":0,"87327414":[],"87381451":5,"875":1,"8759":13,"876":6,"87625493":[],"87647541":[],"8768":[],"878297":[],"87836018":[],"87940752":16,"87it":6,"88":[2,13],"88017651":11,"880177":11,"88031314":[],"88046261":5,"8805555555555555":1,"88118231":[],"88168312e":6,"881788":[],"883":[],"88336879":5,"884":[],"88477619":[],"885":[],"88512489":[],"88529063e":6,"8855417382991412":[],"88554174":[],"886":[],"88679947":[],"887":[],"8879":6,"88871662":16,"8888888888888888":1,"8894":6,"89":2,"8916666666666667":[],"89274639":16,"89288636":11,"893489":5,"89348922":5,"89410423":5,"8944444444444445":1,"89550839":16,"89565264":[],"8962476":[],"896248":[],"89873772":16,"89876748":[],"8991514":16,"89944994":5,"899450":5,"8f":6,"8g":6,"8n":16,"8x8":1,"9":[0,1,2,3,4,5,6,7,8,9,11,12,13,14,16,18],"90":[1,2,6],"900449":[],"90054363":[],"90075537":5,"9011":6,"90220243":5,"90266948":5,"90268858":[],"9027777777777778":1,"903824":[],"90382431":[],"9040":9,"90475506e":6,"90540529":11,"9055555555555556":1,"9083333333333333":[],"90884627":16,"90895045":16,"90940378":16,"90999452e":18,"91":19,"910":[],"9111111111111111":1,"91128596":5,"912u":[],"913":[3,4],"91379157":[],"913791573406831":[],"91383439":16,"91396388":[],"914":[3,4],"91416375":[],"91479093":[],"91492986e":6,"915":[3,4],"91549644":[],"916":[3,4],"9166666666666666":[],"917":[3,4],"91760278":5,"918":[],"91812702":5,"918992":[],"919":[],"92":[0,6,19],"92272314":0,"922u":[],"92343595":[],"924018":[],"92477093":[],"924e":6,"925":1,"92507116e":1,"9252772":11,"92578916":5,"92603747":16,"92626212":16,"92754397":[],"927544":[],"9277777777777778":1,"92819235":[],"92857143":7,"93":[],"9305555555555556":1,"930583":11,"931":0,"93155188":5,"93158979":5,"93248252":5,"932483":5,"932u":[],"933":5,"93414191":[],"93492130e":6,"93579127":16,"935u":[],"9361111111111111":[],"937":18,"937082":[],"93799826":5,"938":18,"9387":[],"939":[0,18],"94":[7,18],"9400":[],"941866404575299":[],"94226022e":6,"94284104":5,"94320205":5,"9444444444444444":1,"945":[3,4],"94548496":[],"94591015":16,"946":[3,4],"94639099":11,"94659383":[],"947":[3,4],"9472222222222222":1,"94735055":[],"947903":[],"94790323":[],"948":[3,4],"9482527":5,"949":[3,4],"95":[1,7,11],"95008046":6,"95079764":6,"95231424":5,"9527777777777777":1,"95284275":5,"953065564":[],"95351665":5,"954":18,"9549351910143222":[],"954u":[],"9555555555555556":1,"95569422":[],"955820c21e8b":4,"956563":11,"95684892":5,"95703":13,"95714723":[],"957147232685324":[],"95it":[],"96":[6,7,11],"960":18,"9601304850018328e":6,"960130485007504e":6,"960130485007934e":[],"9601304850213484e":[],"96024953":5,"96032148":16,"96084663":5,"961":18,"962":18,"9637117593816477":6,"9640435":5,"96489434":[],"9649652536":4,"96527903":16,"96551427e":18,"965548":[],"96606158":16,"96653373":[],"96686324e":18,"96688672":5,"9674916":5,"967809":11,"96863851":[],"96987657":[],"96992454":[],"97":7,"97005689":5,"97065296":[],"97108e":13,"9722222222222222":1,"97230501":[],"97243128":5,"97300836":5,"97497404e":6,"975":1,"97507735":5,"97547354":18,"97547354476579":18,"975510299261579":9,"9760832":[],"97705827":5,"977418":5,"97758848":5,"9777777777777777":1,"977880":[],"97788031":[],"9780387310732":20,"9780387848570":20,"9781492032632":20,"97898392":6,"97926491":5,"98":[0,1,7],"980":[],"98017611":13,"98036405":[],"9805555555555555":1,"98091621":5,"981321":[],"98139097":5,"98266587":16,"98275501":5,"983310":[],"98346748":[],"98399675":[],"98413059":5,"98454786":5,"985":18,"98566191":5,"986":18,"9861111111111112":1,"98680716":5,"98716878":5,"98756882":13,"98794823":16,"9879924":[],"98808176":5,"98822371":6,"9888888888888889":1,"989":18,"9890348":5,"9893149172528393":[],"9893447":5,"9898254753574576":[],"98982548":[],"9898ff":[9,10],"99":[6,7,11,13],"990":[],"99009525":5,"9902552771282336":[],"99049330":6,"99088801":5,"991":18,"99115119":5,"99176998":5,"992":18,"99242921":5,"99265097":5,"993":18,"99316252":5,"99353454":[],"993535":[],"99371056":5,"99389612":5,"993972":[],"99397245":[],"9943201":5,"99435648":[],"9947756":5,"99492986":5,"99519225":13,"99528218":5,"99539415":5,"9955282554647219":[],"99566069":5,"99578809":5,"996":5,"99608161":5,"9963961":5,"99650061":5,"9967458":5,"99700706":5,"99709215":5,"99724883":[],"99729756":5,"99751458":5,"99758326":5,"99775587":5,"99775949":[],"99793613":5,"99799099":5,"99813653":5,"99828624":5,"99829953":[],"9983295":5,"99845267":5,"998577":5,"99861053":5,"99871521":5,"99881845":5,"99884384":5,"99893323":5,"999":[9,18],"99901896":5,"99903755":5,"99911427":5,"99918546":5,"99919837":5,"99926459":5,"9993237":5,"99933188":5,"99938942":5,"9994385":5,"99944272":5,"99949306":5,"99953381":5,"99953475":5,"99957911":5,"99961294":5,"99965056":5,"99967865":5,"99970988":5,"9997332":5,"99975913":5,"99977416":[],"99977849":5,"99980002":5,"9998161":5,"99984732":5,"99987324":5,"99989476":5,"99991263":5,"99992746":5,"99993978":5,"99995":5,"9999555851685968":6,"999955585168597":6,"9999840939906267":[],"9999858320366368":[],"9x":6,"9y":6,"\u00f8yvind":[6,19],"abstract":1,"boolean":4,"break":[0,4,6,11,14],"byte":16,"case":[0,1,2,3,4,5,6,7,11,12,13,14,15,16,17],"catch":0,"char":[],"class":[0,1,3,4,6,7,8,9,11,12,13,18],"const":[],"default":[0,1,2,4,6,7,13,16],"do":[0,2,3,4,5,6,8,9,10,11,12,13,14,16],"ekstr\u00f8m":4,"eng\u00f8i":19,"export":9,"f\u00f8470":19,"final":[0,1,2,3,4,5,6,7,8,9,10,11,13,14,17,18,19],"float":[0,3,4,5,9,11,13,14,16],"function":[2,3,4,5,9,14,15,16],"import":[0,1,2,3,4,6,7,8,9,10,11,12,13,14,18],"int":[0,1,2,3,4,5,6,11,13,14,16,18],"long":[0,1,3,4,12,13],"m\u00f8svatn":6,"new":[0,1,2,3,5,6,7,8,9,10,11,13,14,16],"null":[],"public":[0,15],"return":[0,1,2,3,4,5,6,7,8,9,11,13,14,16,18],"s\u00f8rli":[],"s\u00f8rlie":19,"sch\u00f8yen":[6,19],"short":[4,5],"steinsv\u00e5g":[],"super":[3,5],"switch":0,"throw":[3,6,18],"true":[0,1,2,3,4,5,6,7,8,9,10,12,13,14,16,18],"try":[0,1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,18],"var":[5,6,10,11,18],"while":[0,1,3,4,5,6,7,8,9,11,12,13,18],A:[2,3,5,6,7,10,11,12,13,15,16,17,18,19,20],AND:2,And:[0,3,4,5,6,9,15,18],As:[0,1,2,3,4,5,6,8,10,12,13,16,18],At:[0,4,6,13],BE:0,Be:[2,15],Being:13,But:[0,1,2,3,5,6,9,10,18],By:[0,3,5,6,12,13,16],For:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20],IF:6,IN:20,If:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,18],In:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18,20],Is:11,Ising:[5,12],It:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],Its:[1,2,4,11],NO:[7,11],No:[3,4,5,6,7,8,9],Not:[0,1,5,6,17],OR:18,Of:18,On:[0,3,17,18,20],One:[0,1,3,4,5,6,7,8,11,12,13,18],Or:[0,1,6],Such:[6,12,18],That:[0,5,7,10,11,12,14,18],The:[4,10,13,14,16,17,18,19,20],Then:[0,1,5,6,8,9,10,11,12,13,14,16],There:[0,3,4,5,6,8,9,11,12,14,16,17,18,19],These:[0,3,4,5,8,9,10,11,12,13,14,16,18],To:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,16,18],With:[0,5,6,8,9,10,11,12,14,16,18],_0:[5,8,10,11,13],_1:[2,5,6,8,10,11,12,13,14,16],_2:[2,5,8,11,12,13,16],_3:16,_4:16,_9:13,_:[0,1,2,4,5,6,7,8,9,10,11,12,13,16],_________________________________________________________________:4,__call__:[3,4],__class__:10,__doc__:6,__future__:[8,9],__getitem__:[],__init__:[1,3,4,14],__main__:2,__mosek:5,__name__:[2,10],__traceback__:[3,4],_asarrai:[],_auto10:[6,12],_auto12:6,_auto1:[2,3,4,5,6,7,12,13,16,18],_auto2:[2,3,4,5,6,12,13,16,18],_auto3:[3,4,5,6,12,13,16],_auto4:[4,6,12,13,16],_auto5:[4,6,12,13,16],_auto6:[4,6,12,16],_auto7:[4,6,12,16],_auto8:[6,12],_auto9:[6,12],_ax:[],_base:8,_build:[15,20],_build_call_output:[3,4],_c:1,_call:[3,4],_call_flat:[3,4],_check_1d:[],_check_optimize_result:[7,11],_compon:11,_coordinate_desc:6,_copy_docstring_and_deprec:[],_decor:0,_depth:9,_fraction:9,_get_lin:[],_getitem_multilevel:[],_handl:[3,4],_i:[0,1,2,5,6,8,11,12,13],_inference_funct:[3,4],_interpolatefunctionerror:[3,4],_is_primit:[],_j:[0,1,2,3,5,6,8,13],_jit_compil:[3,4],_k:13,_l:12,_lambda:6,_leaf:9,_logist:[7,11],_m:10,_make_vjp:[2,13],_maybe_define_funct:[3,4],_multilayer_perceptron:1,_n:[2,5,8,11,13],_node:[2,9,13],_notokstatusexcept:[3,4],_np:[],_num_output:[3,4],_p:[5,8],_plot_arg:[],_process_traceback_fram:[3,4],_r:[3,4],_ratio:11,_sampl:9,_select_forward_and_backward_funct:[3,4],_split:[6,9],_src:[3,4,14],_stateful_fn:[3,4],_stateless_fn:[3,4],_t:13,_test:6,_trace:[2,13],_valu:[2,13],_varianc:11,_weight:9,a0:3,a0faa0:[9,10],a1:0,a2:0,a3:0,a4:0,a_0:0,a_1a:0,a_2a:0,a_3:0,a_3a:0,a_4:0,a_4a:0,a_:[0,1,16],a_h:1,a_i:[0,1,2,12],a_j:[1,12],a_k:[1,12],a_ndim:2,aaron:20,ab:[0,2,5,13,14],ab_channel:15,abandon:1,abbrevi:17,abid:18,abil:[0,10],abl:[1,4,5,6,7,10,12,13],abort:[],about:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,20],abov:[0,1,3,4,5,6,7,8,9,10,11,12,13,14,16,18],abovement:6,abscissa:13,absolut:[0,2,5,6,13],acceler:13,accept:[0,3,6,9],access:[0,3,4,11,18],accid:[4,6],accmod:5,accompani:0,accomplish:[8,9,13],accord:[0,1,2,5,6,9,12,13,14,18],accordingli:11,account:[0,3,5,13,18],accumul:[12,18],accur:[0,3,4,6,10,13],accuraci:[0,1,3,4,5,6,7,9,10,11,12],accuracy_scor:[0,1,10],accuracy_score_numpi:1,achiev:[0,1,5,6,8,12,16],aco:18,acquaint:15,acquir:[1,15],acr:0,across:[1,3,6,9,15],act:[1,3,16],action:18,activ:[0,2,3,4,9,17],actual:[0,1,4,5,6,8,11,16,18],ad:[0,1,3,4,5,8,13,16],ada_clf:10,adaboostclassifi:10,adadelta:13,adagrad:13,adam:[1,3,4],adapt:[4,6,13,20],add:[0,1,2,3,4,5,6,8,10,11,12,18],add_lin:[],add_outgrad:2,add_subplot:[1,7,12,14],addendum:5,addit:[0,2,3,5,6,7,8,9,10,12,13,15,16,18,19,20],addition:[12,13],address:[1,9,11,13,20],adjac:[3,12],adjoint:5,adjust:[0,5,12,13],admir:0,advanc:[4,6,12,20],advantag:[1,3,5,6,10,13,16],affect:3,affin:[0,3,8,11],afford:3,aforement:14,african:0,after:[0,1,2,4,5,6,9,11,12,13,15,16,18],afterward:0,ag:[0,7,17],ag_0:2,again:[0,1,4,5,6,7,8,10,11,12,13,18],against:[1,4,7,10],agegroup:7,agegroupmean:7,aggreg:[9,10],agorithm:10,agre:[5,6,18],ahead:9,ai:[0,20],aid:11,aim:[0,1,4,6,7,11,14,15,16],ainv:5,airplan:3,aka:5,al:[0,2,4,17,20],alarm:5,algebra:[0,3,5,13,15,17],algo:[],algorithm:[0,1,2,4,5,6,7,8,13,14,15,16,17,18,20],align:[0,2,5,6,7,8,13,18],all:[0,1,2,3,4,5,6,7,9,10,11,12,13,14,15,16,17,18,19,20],allevi:[1,13],alloc:[3,16],allow:[0,1,2,3,5,6,8,10,13,15,16],almost:[0,1,6,8,11,13,18],alon:[2,9],along:[2,3,4,5,6,9,10,11,15,16],alpha:[0,1,2,3,4,5,6,7,8,9,10,13,14,18],alpha_0:3,alpha_1:3,alpha_2:3,alpha_:10,alpha_i:[3,13],alpha_k:13,alpha_m:10,alpha_n:3,alpha_opt:13,alreadi:[2,3,4,5,6,10,12,15,16,18],also:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20],alter:1,altern:[0,1,4,5,6,7,8,9,11,13,16],although:[1,5,6,8,10,13],alwai:[0,3,5,6,12,13,18],am:4,ame2016:0,american:0,among:[0,3,5,9,10,12,16],amongst:5,amount:[0,1,3,4,6,8,10,14,15],an:[1,2,3,5,6,7,8,9,11,12,13,14,15,16,18,19,20],an_:18,anaconda3:[],anaconda:[0,1,15],analog:13,analys:6,analysi:[1,3,4,7,14,16,17,20],analyt:[0,2,3,5,6,7,12,13,15],analyz:[0,1,3,4,5,6,18],andrew:1,angl:[0,3,9],anharmon:3,ani:[0,1,2,3,4,5,6,7,8,9,10,12,14,18],anim:[4,12],ann:12,annot:[0,1,3,7,8],anoth:[0,1,3,4,5,6,7,8,10,11,12,13,16,18],anp:2,ans_vspac:2,ansatz:0,answer:[0,1,3,5,6,16],antialias:[2,6],anticip:4,anymor:[1,8],anyon:[4,8],anyth:[1,18],anytim:19,apach:1,apart:[11,13],api:[1,15],appar:2,appear:[0,1,3,13,16,18],append:[1,3,4,8,9,13],appendcon:5,appendvar:5,appli:[0,1,2,3,4,6,7,8,9,10,11,12,13,18,20],applic:[0,1,3,4,5,6,7,9,12,13,16,17,18,20],apply_gradi:4,approach:[1,2,4,5,6,9,10,11,12,13,15,18,20],appropri:[2,6,9,12,13,15,18],approx:[0,2,3,6,10,11,13,18],approxim:[0,1,2,3,4,5,6,7,10,11,13,18],apt:[0,15],aq:18,ar:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20],arang:[1,3,4,6,7,9,10,12],arbitrari:[1,4,6,8,12,13,18],arbitrarili:[0,1,11],arc:6,architectur:[3,4,12,20],area:[0,3,6,20],arg:[0,2,3,4,13],argc:[],argmax:[1,11],argmin:[4,10,14],argnum:[2,13],argnum_0:2,argnum_1:2,argsort:11,argu:[1,13],argument:[0,2,3,5,6,11,12,13],argv:[],argval:2,aris:[0,6,12,13,18],arithmet:[0,13,16],arm:[3,4,6,14],arma:[],armadillo:16,around:[0,1,4,5,6,11,18],arr:2,arrai:[0,1,2,3,4,5,6,7,8,9,11,12,13,14,15,18],arrang:3,arraybox:13,arriv:[0,6,9,11,16,18],arrow:12,arrowprop:8,art3d:13,art:[0,1,15],articl:[0,3,4,6,10],artifici:[0,2,7,12,20],artificialneuron:12,arug:13,arxiv:[3,4],as_fram:[],asarrai:[0,2,6,9],asc:5,ascii:[],ashrafi:19,ask:[5,6,11,12],aspect:[0,6,15],assembl:[0,3],assert:4,assess:[0,6],assici:4,assign:[0,7,8,9,12,13,14,17,20],associ:[0,6,9,12,14,18],assum:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],assumpt:[0,3,5,6,9,11,18],ast:[0,5,6],astyp:[4,9,10],asymmetri:0,asymptot:[4,6],async_wait:[3,4],atoi:[],atom:0,attempt:[0,4,6,7,8,10],attend:17,attent:[0,16],attr:[3,4],attract:[0,10],attribut:[0,9],attributeerror:[],audi:0,audio:[3,4],aurelien:[0,17,20],austfjel:6,author:[0,1,10,18],authour:0,auto:[6,9,10,18],autocor:18,autocorrelation_tim:18,autocorrelform:18,autocovari:18,autoencod:[4,15],autoencond:15,autograd:15,autom:[0,15],automac:16,automat:[0,1,2,3,4,11,15,16,17],automobil:3,autonom:[4,20],avail:[0,1,4,6,10,11,15,16,17,20],averag:[0,1,3,6,9,10,13,14,18,19],avg:[],avoid:[0,4,5,6,9,11,13,16],avx2:[],avx:[],awai:[2,3,6],awar:[2,10],award:19,ax:[0,1,2,3,4,6,7,8,9,10,11,12,13,14,16],axes3d:[2,6,13],axes_grid1:6,axessubplot:[],axhlin:8,axi:[0,1,2,3,4,6,7,8,9,10,11,12,13,14,16,18],axiom:5,axlabel:0,axvlin:[4,8],axvspan:4,b1:8,b2:8,b3:8,b:[0,1,2,3,4,5,6,8,9,10,12,13,14,18,19],b_1:[2,12,13],b_2:13,b_5:13,b_:[1,16],b_group:9,b_i:[0,1,2,12],b_ia_:0,b_index:9,b_is_vec:2,b_j:[1,12],b_k:[1,12,13],b_m:12,b_meta:2,b_score:9,b_valu:9,bachelor:17,back:[0,3,4,5,6,8,9,10,16,17,18],backbon:16,backend:[1,4],background:[17,20],backpropag:1,backtrack:9,backup:16,backward:[1,2,4,12,16],backward_pass:2,bad:6,badli:18,bag:[9,15,17],bag_clf:10,baggingboot:10,baggingclassifi:10,baggingtre:10,balanc:6,band:16,bandwidth:16,bar:[0,6,11],barber:20,bare:[4,10],base:[0,1,3,4,5,7,8,9,10,14,15,18,19,20],basi:[5,7,8,10,11,12,13,16],basic:[6,8,12,13,14,15,17,18],batch:[3,4,11,12,13],batch_shap:4,batch_siz:[1,3,4],batchnorm:4,bay:7,bayesian:[5,15,20],bc298b802fe2:6,becaus:[0,1,2,3,4,5,6,8,9,12,13,14],becom:[0,1,2,5,6,7,9,12,13,18],been:[0,1,2,3,4,5,6,11,12,13,15,16],befor:[0,1,2,3,4,5,6,7,8,12,13,14,16,18],beforehand:[0,18],begin:[0,1,2,3,4,5,6,7,8,9,11,12,13,14,16,18],behav:[1,6,13],behavior:[0,1,13],behaviour:12,behind:[0,1,6,8,13],behnoosh:19,being:[0,1,2,3,4,5,7,8,10,11,12,13,18],believ:[9,16],belong:[5,7,8,9,13,14],below:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,16,18],benchmark:10,bendik:[],benefici:[1,13],benefit:[0,1,4,11,13,15],bengio:[1,17,20],benign:[1,7],bennosh:19,besid:[4,5],bessel:5,best:[0,1,2,3,4,5,6,7,8,9,10,12,13,19],beta:[0,1,3,5,6,7,10,11,13],beta_0:[0,1,3,5,6,7,13],beta_0x_:0,beta_1:[0,1,3,5,6,7,10,13],beta_1x_0:0,beta_1x_1:[0,7],beta_1x_2:0,beta_1x_:0,beta_1x_i:[7,13],beta_2:[0,3,13],beta_2x_0:0,beta_2x_1:0,beta_2x_2:[0,7],beta_2x_:0,beta_3:3,beta_:[0,3,6,7,13],beta_i:[0,3,5],beta_j:[0,5,6,13],beta_k:13,beta_linreg:13,beta_m:10,beta_mg_m:10,beta_n:3,beta_p:7,beta_px_p:7,betavalu:5,better:[0,1,2,3,4,6,9,10,11,12,13],between:[0,1,2,3,4,5,6,7,8,9,11,12,13,14,18],beyond:[0,1,5,6,8,13],bf:[13,14,16,18],bgd:13,bia:[0,1,2,3,5,8,9,10,12,13,17],bias:[1,2,3,5,6,9,12],big:[0,1,2,5,6,14],bigger:[1,6],bigr:12,bike:9,bilek:19,billion:[3,12,15],bin:[0,7,18],binari:[0,3,5,7,9,10,12,17],binarycrossentropi:4,bind:0,binomi:[15,18],binsboot:6,bioinformat:0,biolog:[1,12,20],bios1100:15,bird:[0,3],bishop:[17,20],bit:[1,4,16,18],bitwis:18,bivari:2,bk:[0,13],bla:[5,16],black:[8,9,14],block:[6,10,15,16,18],blockingavg:[],blockingstd:[],blockingvar:[],blocksiz:[],blocksizemax:[],blocksizemin:[],blogpost:4,blue:[0,3],bmatrix:[0,1,3,5,7,8,11,13,16],bmi:1,bodi:[0,1,4,12],bold:1,boldfac:[0,5],boldsymbol:[0,1,2,3,5,6,7,8,10,11,13,14],boltzmann:[12,15],book:20,boost:[1,9,15,17],boostrap:10,bootavg:[],bootstd:[],bootstrap:[1,13,15,17],bootvar:[],bootvec:[],boston_dataset:0,bot:8,both:[0,1,4,5,6,8,9,10,13,14,15,16,18,19],bottl:7,bound:[0,5,8,12],boundari:[2,4,8,11,12],boundkei:5,box:[2,4,9],boxed_arg:2,boyd:[8,13],bracket:[4,18],brain:[1,7,12],branch:9,breast:[5,7,11],breviti:13,brew:[0,15],brg:8,briefli:0,bring:[0,5,6,10],broad:[0,3,4],brownle:4,brute:[3,5,11],bs:[8,9,10],buffer_s:4,bui:4,build:[0,4,5,6,10,13,16,18],built:[0,1,3,4,6],bunch:11,busi:0,bx:5,bzl:5,c1:[8,11],c2:[8,11],c95af3df0cdd:3,c:[0,1,2,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19,20],c_0:18,c_1:12,c_2:12,c_3:12,c_4:12,c_:[0,8,9,10,13,18],c_i:[12,13],c_k:18,ca:1,cabc613b8702:13,cach:10,cal:[0,8,10,12,13],calcul:[0,1,2,4,5,6,8,9,10,11,12,13,14,16,18],california:[],call:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,20],callabl:[3,4],callback:[3,4],calor:0,cambridg:[13,20],can:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,20],cancel:[0,13],cancellation_manag:[3,4],cancer:[5,10],cancerpd:7,candid:[8,9,10],cannot:[0,1,4,5,6,7,8,9,17,18],canopi:[0,15],cap:5,capabl:[0,1,8,13,15],capac:2,capita:0,captur:[4,11,12],captured_input:[3,4],car:[3,4],card:[0,7],cardin:1,care:11,carefulli:13,carlo:[0,6,15,18,20],carri:[2,6,7],cart:10,carvalho:19,casella:20,cast:1,cat:[3,4],categor:[0,1,3,9,11],categori:[0,1,3,7,10,12,14,17],categorical_crossentropi:[1,3],caus:[0,5,6,18],causal:0,causat:0,cax:1,cb:6,cbar:1,cbook:[],cc:[0,1,5,13],ccc:[5,12],cd_fast:6,cdf:18,cdot:[0,2,6,12,13,14,16,18],celebr:13,cell:4,center:[0,1,6,7,8,9,11,14,18],centr:20,central:[0,3,5,6,8,16],centroid:[14,18],centroid_differ:14,centuri:3,certain:[0,3,6,7,9,18],cg:13,cha:0,chain:[1,13,15,18],chanc:[1,5,13,18],chang:[0,1,2,3,4,5,6,8,9,11,12,13,14,16,18],channel:3,chapter:[0,6,10,11,16,17,20],charact:[0,3,5],character:[8,9,10,12,18],characterist:[0,1,3,10,13],charg:0,charl:0,chase:4,chd:7,chddata:7,cheap:5,cheaper:[1,13],check:[0,1,3,4,5,6,11,13,16],checkmark:3,checkpoint:4,checkpoint_dir:4,checkpoint_prefix:4,chemic:[],chen:10,chiaramont:2,choic:[0,1,2,3,4,6,9,12,13,14,16],choleski:[5,16],choos:[2,3,6,9,10,11,13,14],chosen:[0,1,2,6,8,9,10,13,18],chosen_datapoint:1,christian:20,christoph:[17,20],cifar10:3,cifar:3,cin:[],circ:[1,12],circl:[0,8,12],circuit:3,circumfer:9,circumv:[1,5,13],ckpt:4,clariti:18,class_nam:[3,9],class_val:9,class_valu:9,class_weight:[3,4],classic:[7,9,13],classif:[0,3,5,6,7,8,11,12,15,17,20],classifi:[0,1,4,7,9,10,11],classificaton:1,classifii:10,clean:1,clear:[1,5,10,12,13],clearli:[0,3,5,6,7,8,18],clever:[1,10],clf3:0,clf:[0,6,8,9,10],clf_lasso:6,clf_ridg:6,clip:[3,18],close:[0,1,2,4,6,8,9,11,12,13,14,18,20],closer:[3,5,13],closest:[8,11,13,14],closur:15,cloud:15,cluster:[0,1,4,6,11,15,17],cluster_label:14,cm:[1,2,3,6,8,13],cmap:[0,1,2,3,4,6,8,9,10],cmap_arg:6,cmath:[],cmb:17,cmd:9,cmu:[],cn_:18,cnn:[12,17],cnn_kera:3,cntk:15,co:[0,2,3,6,9,13],code:[3,4,6,7,8,13,15,16,17,18,20],coef0:8,coef:0,coef_:[0,5,6,8,9,13],coeff:5,coeffici:[0,3,5,6,7,8,9,13,16],coerc:[0,6],coin:[10,18],coin_toss:10,col:[0,11],colab:15,cold:9,colinear:0,collaps:8,collect:[0,2,6,10,11,15,18,20],collinear:5,color:[0,3,4,6,8,9,10,18],color_channel:3,color_cod:6,colorbar:[1,6],colsample_bytre:10,colsaobject:10,colspec:0,column:[0,1,2,5,6,7,8,9,11,12,16],columntransform:9,com:[3,4,6,14,15,20],combin:[1,2,5,6,7,10,18],come:[0,1,3,4,5,12,13,14,17],command:[0,1],comment:[0,4,5,6],commerci:[0,15],commod:0,common:[0,1,3,5,6,7,9,11,13,14,18],commonli:[0,1,4,6,7,9,13,14],commun:[0,12],commut:3,commutatitav:3,compact:[0,1,3,5,6,7,9,11,12,13,14],compar:[0,3,4,5,6,11,13,16],comparison:[2,4,13],compat:7,compet:0,competit:10,compil:[0,1,3,4,15,16],complet:[0,2,3,4,9,12],completenn:12,complex:[1,5,8,9,11,12,13],complic:[0,1,9,13],compon:[0,1,3,4,5,6,7,9,14,15,17],components_:11,compos:[9,12,14],compphys:[6,15,17,20],compress:0,compris:6,compromis:5,compulsori:15,comput:[0,1,2,3,4,5,6,7,8,10,11,12,13,15,16,17,18,20],computation:[0,3,6,9,13,18],con:5,concaten:[2,4,6,14],concav:[1,13],concentr:[0,10],concept:[0,2,15],conceptu:[12,13],concern:[0,1,4,7],conclud:[0,5,13],conclus:1,cond:2,conda:[0,1,15],condit:[0,2,4,5,6,8,9,11,13,18],conduct:15,condwav:2,coneqp:5,confid:[0,5,6,7,8],configur:3,confirm:[5,12],confus:[5,6,10,16],confusion_matrix:9,congruenti:18,conjug:[4,8],conjugaci:13,conjunct:3,connect:[0,1,3,4,9,11,12,13,16],consequ:[5,6,8,10,12,13],conserv:[5,14],consid:[0,1,2,3,5,6,7,8,9,10,12,13,16,18],consider:[0,1,5,13],consist:[0,1,2,3,4,6,12,13,18],constant:[0,2,4,5,6,8,12,13,18],constitu:0,constitut:[2,6],constrain:[1,3,5,7,11],constraint:[5,6,8,13],construct:[0,1,2,3,5,6,7,8,9,10,11,16,18,20],contact:0,contain:[0,2,3,4,5,6,7,8,9,11,12,13,16,18,20],contemporari:20,content:[1,15,16],context:[3,4,6,10,13],contigu:16,continu:[0,1,2,3,4,5,6,7,8,9,10,12,13,16,18],contour:[9,10,13],contourf:[8,9,10],contrast:[1,4,9,10,12],contribut:[0,3,5,13,18],contributor:0,control:[0,1,3,9,13,15],conv2d:[3,4],conv2dtranspos:4,conv:[3,4],conveni:[0,5,6,12,13,16],convent:12,converg:[1,2,4,5,6,7,8,11,13,14],convergencewarn:[1,6,7,8,11],convert:[0,1,4,5,9,11,13,16],converttomatrix:4,convex:[4,5,7],convinc:13,convolut:[1,4,15,17],cool:[4,9],coolwarm:6,coordin:[5,12,14],coorel:0,copi:[0,1,14],core:[2,3,4,10,13],corel:0,coronari:7,corr:[0,5,7,11],correalt:[11,15],correct:[0,1,2,3,4,5,13,16,18],correctli:[1,2,6,10],correl:[0,1,3,5,6,7,10,12,13,15,18],correlation_matrix:[0,5,7,11],correspond:[0,3,5,6,8,9,11,12,15,16,18],cortex:12,cosin:[3,6],cost:[0,2,3,5,6,7,8,9,12,13],cost_deep_grad:2,cost_funct:2,cost_function_deep:2,cost_function_deep_grad:2,cost_function_grad:2,cost_grad:2,cost_sum:2,costol:13,could:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,16,18],coulomb:0,count:[0,9,17,18,19],countor:13,coupl:[4,5,6],cours:[0,1,3,5,11,17],courvil:[17,20],cout:[],cov:[5,6,11,16,18],cov_xi:[5,11],cov_xx:[5,11],cov_yi:[5,11],covari:[0,7,15,16],covariance_matrix:[5,11,14],cover:[0,5,15,17,20],covert:0,covxi:18,covxx:18,covxz:18,covyi:18,covyz:18,covzz:18,cpu:1,cpu_feature_guard:[],craft:3,creat:[1,2,3,4,5,6,9,10,11,12,13,15],create_biases_and_weight:1,create_convolutional_neural_network_kera:3,create_neural_network_kera:1,create_x:[5,11],credit:[0,7],crim:0,crime:0,criteria:[0,4,9,10,14,18],criterion:[9,10,13],critic:6,cross:[0,1,3,7,9,10,13,15,17,18],cross_entropi:4,cross_val_scor:6,cross_valid:[7,10],crossvalid:6,crucial:[1,18],cs231:3,cs:17,csr_matrix:16,cstdlib:[],csv:[0,4,6,7,9],ctnk:1,ctx:[3,4],cubic:0,cumbersom:5,cumsum:[10,11],cumul:[10,18],cumulative_heads_ratio:10,cup:5,current:[1,2,3,4,6,13,14],curs:0,curv:[6,7,10,12],curvatur:13,custom:[5,6,14],custom_cmap2:[9,10],custom_cmap:[9,10],cutpoint:9,cv:[6,7,10],cvxbook:13,cvxopt:[5,8],cyber:20,cycl:[1,12],d1:5,d2:5,d2_g_t:2,d3:5,d670a873ab0c:5,d985fb40c43d:6,d:[1,2,3,4,5,6,7,8,9,10,11,13,14,16,18,19],d_f:13,d_g_t:2,d_net_out:2,da:3,dagger:[5,16],dai:[1,9,15],dalen:[],damp:3,darget:9,darkr:18,dat:0,dat_id:[0,6,7,9],data1:14,data2:14,data3:14,data4:14,data:[2,4,5,8,10,12,13,14,16,20],data_handl:[3,4],data_id:[0,6,7,9],data_indic:1,data_modul:[],data_path:[0,6,7,9],data_url:[],databas:1,datafil:[0,6,7,9],datafram:[0,4,5,7,9,11],datapoint:[1,5,6,7,11,13],dataset:[0,4,6,7,8,9,10,11,13,14],datatyp:4,date:0,daughter:10,david:20,dbh:1,dbo:1,dcomposit:16,ddot:2,dead:1,deadlin:17,deal:[0,1,3,5,6,8,11,13,14,16,18],debt:7,debug:[5,6],decad:[0,3],decai:[0,13,18],decemb:17,decent:10,decid:[0,2,3,5,6,9],decim:0,decis:[0,1,8,11,15,17,20],decision_funct:8,decision_tre:9,decisiontreeclassifi:[9,10],decisiontreeregressor:[0,9,10],declar:[0,4,16],decompos:[5,6,16],decomposit:[0,6,12,17],decompost:5,deconvolut:3,decorrel:[10,13],decreas:[1,2,4,5,6,10,11,13],deduc:0,deep:[3,7,12,13,15,17,20],deep_neural_network:2,deep_param:2,deep_tree_clf1:9,deep_tree_clf2:9,deep_tree_clf:[9,10],deepen:[5,15],deeper:[0,3,4],deeplearningbook:20,deer:3,def:[0,1,2,3,4,5,6,7,8,9,10,11,13,14,18],def_covari:18,def_funct:[3,4],default_tim:4,defect:5,defici:5,defin:[0,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],definit:[1,2,5,6,8,10,11,12,13,16,18],defint:18,defun:[3,4],defvjp:2,degre:[3,5,6,8,9,10,11,18],del:1,delet:6,delimit:4,deliv:17,delta:[0,2,3,6,8,12,13,14],delta_0:3,delta_1:3,delta_2:3,delta_3:3,delta_4:3,delta_5:3,delta_:[1,16],delta_h:[0,1],delta_j:[3,12],delta_k:12,delta_l:[1,3],delta_n:[0,3],delug:15,delv:0,demand:13,demonstr:[0,3,5,6,7,11,12,15],den:4,denomin:[1,5],denot:[1,2,6,7,13,18],dens:[1,3,4],dense_1:4,densiti:[0,2,6,18],depart:19,depend:[0,1,2,4,5,6,7,8,11,12,13,15,16,18],depict:18,deploy:[0,15],deprec:[2,3,6,13],depth:[0,3,9,10,16],deriv:[0,1,2,6,7,8,10,11,13,15],derivati:13,descend:[5,9,11],descent:[0,1,3,7,8,12,17],descr:[],describ:[0,2,4,5,6,8,10,11,12,13,16],descript:[0,8,9],design:[0,1,3,4,5,6,7,10,11,12,13],designmatrix:0,desir:[0,2,4,5,13,14],despit:[1,12],destroi:16,det:[5,16],detail:[0,6,11,13,14,16],detect:[3,8,12],determin:[0,2,3,4,5,6,8,9,10,11,12,13,16,18],determinist:[7,13,18],dev:1,develop:[0,3,5,8,10,11,12,15,16,17],deviat:[0,1,2,4,5,6,18],device_nam:[3,4],devis:12,df:[4,8,11,13],di:[0,5],diag:[5,8],diagnost:[1,10],diagon:[0,5,7,13,16,18],diagonaliz:5,diagram:10,diagsvd:6,dice:[6,18],dict:[6,8],dict_kei:[],dictionari:0,did:[0,1,5,6,7,10,11,14],die:1,diff1:2,diff2:2,diff:2,diff_ag:2,diffeent:8,differ:[0,1,2,3,4,5,6,9,10,11,12,13,14,15,16,18,20],differenti:[3,15,16,17],differential_oper:[2,13],difficult:[0,1,6,10,13,18],difficulti:[0,1,13],diffonedim:2,digit:[0,1,3,4,6,17,19],dilemma:13,dilut:1,dim:[4,11,14,16],dimens:[0,1,2,3,4,5,8,11,14,16],dimension:[0,4,5,6,9,11,13,14,15,16],dimensionless:[0,3],diment:16,dimnsion:4,diod:3,direct:[0,1,2,4,11,12,13,14],directli:[1,4,5,6,18],directori:[],disabl:[3,4],disadvantag:0,disappear:[3,6],disc_loss:4,disc_tap:4,discard:[6,11],disciplin:[0,3,12],disclaim:18,discourag:13,discov:0,discover:5,discret:[1,3,5,7,13],discrimin:[4,7,10,11],discriminator_loss:4,discriminator_loss_list:4,discriminator_model:4,discriminator_optim:4,discuss:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20],diseas:7,disguis:6,disord:[1,7],displai:[0,1,3,4,5,6,7,8,9,10,11,12,14,18],displaystyl:[0,5],displot:[],disregard:0,dissimilar:[11,14],dist:14,distanc:[0,8,9,11,14,18],distance_list:9,distinct:[3,7,8,9,10,14],distinctli:8,distinguish:[0,4,7,8,18],distplot:0,distribut:[0,1,4,6,7,10,11,13,14,15,16],distrubut:[0,15],div:5,dive:[0,8,16],diverg:[1,13],divid:[0,1,3,5,6,8,9,11,12,18],divis:[6,8,9,13,16,18],dna:7,dnn1:4,dnn2_gru2:4,dnn:[0,1,2,4,12],dnn_kera:1,dnn_model:1,dnn_numpi:1,dnn_scikit:[0,1],doc:[6,15,17,20],document:[4,7,11,13],doe:[0,1,2,3,4,5,6,8,10,11,12,13,16,18],doesn:[3,9,12],dog:[1,3,4],domain:[5,8,13],domin:0,don:[0,1,3,5,6,8,11,13,15],done:[0,2,3,4,5,6,9,10,11,13,16],dot:[0,2,3,5,6,7,8,9,10,11,12,13,16,18],doubl:[3,4,16],doubli:1,down:[0,3,6,9,11,12,13],download:[0,1,3,5,6,16,20],downsampl:3,dozen:1,dq:6,drag:13,dramat:11,drastic:4,draw:[4,6,10,13],drawback:[0,1,3,13],drawn:[1,4,6,7,11,18],drive:[3,4],driven:3,drop:[0,1,5,6,11,13,18],dropna:[0,6],dropout:4,ds:5,dt:[2,3,13,18],dtype:[0,1,2,3,4,14,16],dualiti:6,dub:0,due:[1,2,5,6,8,10,12,13],dummi:0,dure:[0,1,3,4,8,9,11,15],dwell:0,dwh:1,dwo:1,dx:[2,3,8,18],dx_1:18,dx_1p:6,dx_2p:6,dx_mp:6,dx_n:18,dxp:6,dy:[1,8,18],dynam:4,dysth:19,dz:8,e:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,18,19],e_:[0,2],each:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19],eager:[3,4],eapprox:0,earli:[1,3,4,13],earlier:[0,5,7,8,9,11,12,13],earthexplor:6,eas:[6,9,14],easi:[0,5,6,7,8,9,10,11,12,13,15,16],easier:[5,6,8,9,13,18],easiest:13,easili:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,16],eastern:19,ebind:0,eblock:9,economi:5,ecosystem:15,ect:17,edg:3,edgecolor:6,edu:13,educ:0,eface79dac2c:10,eff:18,effect:[1,4,10,13,18],effic:1,effici:[0,3,10,13,15,16,18],efron:6,egrad:13,eig:[5,11,13,16,18],eigen:18,eigenpair:[5,11],eigenvalu:[0,5,8,11,13,16],eigenvector:[5,11,13],eight:16,eigval:[16,18],eigvalu:[11,13],eigvec:[16,18],eigvector:[11,13],eispack:16,either:[1,5,6,7,8,9,10,11,13,18],ekstrom:[],elabor:18,elarn:3,electr:[0,3,12],electur:20,eleg:11,element:[1,2,3,4,5,6,7,8,11,12,13,15,16,17,20],elementari:[10,13,16],elementwis:[3,13],elementwise_grad:[2,13],elif:[2,3,4,14],elim:16,elimin:[3,5,8],els:[1,2,3,4,7,9,12,13,16],elu:1,elus:0,email:[17,19],embed:[0,11],embodi:6,emit:18,emner:[17,20],emphas:[0,10,15],emphasi:[0,15,20],empir:[1,11,18],emploi:[0,1,5,6,11,13,18],employ:0,empti:[6,10],emul:12,en:[15,20],enabl:11,enbodi:6,encod:[0,3,5,9,11,14],encompass:[0,18],encount:[0,1,5,6,7,13,18],end:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],end_box:[2,13],end_nod:[2,13],end_valu:[2,13],endl:[],endpoint:[3,6],energi:[0,4,6],enet_coordinate_desc:6,enforc:12,eng:20,engin:[0,1,3,4,15],english:20,enorm:3,enough:[0,6,13],ensembl:[1,9,17],ensur:[0,1,2,3,5,6,11,13,18],ensure_initi:[3,4],enter:[5,6],enthought:[0,15],entir:[1,3,7,9,15,18],entiti:[9,12,16],entri:[0,5,8,11,12,16],entropi:[1,3,7,10,13],enumer:[0,1,2,3,4,6,8],env:[0,1,2,3,4,5,6,7,8,11,13,14,18],environ:[2,15,20],eo:[0,6],eol:0,eosfit:0,epoch:[0,1,3,4,12,13],epsilon:[0,5,6,7,13],epsilon_0:0,epsilon_1:0,epsilon_2:0,epsilon_:0,epsilon_i:0,eq:[3,13,14,16,18],eqnarrai:[3,5,6],equal:[0,1,2,3,4,5,6,8,9,11,12,13,14,16,18],equat:[1,3,4,5,6,7,8,9,10,11,13,14,16,17,18],equilibrium:[2,12],equiv:[3,13,16,18],equival:[0,1,5,8,11,13,15,16],erf:18,err:[0,10],err_:6,err_sqr:2,errat:13,errno:[],erron:2,error:[1,2,4,5,6,7,9,11,12,13,15,16,18],error_estimate_corr_tim:18,error_handl:[3,4],error_hidden:1,error_output:1,escap:13,especi:[1,3,9,12,13],essenti:[0,5,6,9,10,12,14,17,18],establish:[0,6,10,11],estim:[0,1,5,6,7,10,11,13,15,18],estimated_mse_fold:6,estimated_mse_kfold:6,estimated_mse_sklearn:6,et:[0,2,4,17,20],eta0:[8,13],eta:[0,1,3,8,12,13],eta_:13,eta_t:13,eta_v:[0,1,3],etc:[0,1,3,5,7,8,9,11,12,13,14,15,16,18],ethic:15,etsim:6,euclidean:[0,14],evalu:[0,2,3,4,5,6,9,13,18],evalut:13,even:[0,1,3,4,5,6,8,9,10,11,12,13,14,15,16,18],evenli:4,event:[3,4,5,7,10,14,18],eventu:[5,6,11,12,13,19],everi:[0,1,2,3,4,5,6,9,10,11,12,13,14,15,18],everyth:[4,12],everywher:[4,13],evolv:0,exact:[0,2,5,11,12,13,16,18],exactli:[0,3,4,6,12,15],examin:6,exampl:[5,11,12,13,15,16,17,18,20],exce:[1,12,13],excel:[0,1,4,5,10,20],except:[3,4,5,6,8,9,16],excess:0,excit:0,exclud:[1,6,12],exclus:[0,1,3,6,18],execut:[2,3,4,5,13],executing_eagerli:[3,4],exemplifi:13,exercis:[5,15,17],exhaust:6,exhibit:[0,5,6,8],exist:[0,1,2,3,5,6,7,8,9,13,16,20],exit:[5,16],exp:[0,1,2,5,6,7,8,10,11,12,13,18],exp_term:1,expand:[5,7,11,13],expans:[0,3,5,8,10,12,13],expect:[0,1,5,6,7,11,12,13,15],expectation_value_of_h_wrt_p:18,expens:[6,10,13],experi:[0,1,6,8,13,15],experiment:[0,3,4,6,9,14,18],experimental_get_tracing_count:[3,4],expert:[1,9],explain:[0,6,9,10,11,13],explained_variance_ratio_:11,explanatori:0,explicit:[0,3,6,13,16],explicitli:[0,4],explod:1,exploit:[0,3,12,13],explor:[1,4,6,8,13,15],expon:1,exponenti:[0,1,5,6,10,13,18],export_graphviz:9,export_text:9,exporttext:9,expos:15,express:[0,2,3,5,6,7,10,12,13,16,18],exptmean:18,exptvari:18,extend:[2,5,7,11,13,15],extens:[0,12,15],extent:[0,1,6,20],extern:[3,6,9],extra:[1,3,5],extract:[0,3,5,6,7,8,11,13,16],extrapol:0,extrem:[0,1,4,5,6,7,8,9,13,16],extremum:13,extrins:11,ey:[0,5,6,13,14,16],f11:0,f12:0,f13:0,f1:13,f1_grad:13,f1d:13,f2:13,f2_grad_x1:13,f2_grad_x1_analyt:13,f2_grad_x2:13,f2_grad_x2_analyt:13,f3:13,f3_grad:13,f3_grad_analyt:13,f4:13,f4_grad:13,f4_grad_analyt:13,f5:13,f5_grad:13,f6:13,f6_for:13,f6_for_grad:13,f6_grad_analyt:13,f6_while:13,f6_while_grad:13,f6d7a289d493:[],f7:13,f7_grad:13,f7_grad_analyt:13,f8:13,f8_grad:13,f9:[0,13],f9_altern:13,f9_alternative_grad:13,f9_grad:13,f:[0,1,2,3,4,5,6,7,8,10,12,13,14,16,18,19],f_0:[3,10],f_1:[10,13],f_2:[12,13],f_3:12,f_:10,f_d:18,f_grad:13,f_grad_analyt:13,f_i:[6,12],f_m:[3,10],f_n:3,f_raw:2,f_vec:2,f_wrap:2,face:13,facecolor:[6,8,18],facil:[0,15],facilit:12,fact:[0,1,3,5,9,11,12,13],factor:[0,1,3,5,6,9,10,11,13,16,18],factori:13,fade:6,fafab0:[9,10],fail:[0,6,7,8,11,13,19],failur:7,fairli:[1,2,18],fake:4,fake_loss:4,fake_output:4,fall:[8,9,17],fals:[0,1,2,3,4,5,6,7,9,10,13,14,16],famili:[0,7,8,18],familiar:[0,3,5,6,8,15,16,18],famou:[6,12],far:[0,3,4,5,6,8,11,12,13,14],fashion:[0,9,10],fast:[1,3,6,10,12,13,15,18],faster:[1,11,13],fastest:[13,16],favor:7,favorit:18,fc:3,fdf8a5d7c717:2,fdfcc778e1f8:4,fe5b9d300cc0:6,featur:[0,1,3,5,6,7,8,10,11,12,13,15,18],feature_nam:[0,1,7,9],feautur:9,fed:1,feed:[0,2,3,11,15,17],feed_forward:1,feed_forward_out:1,feed_forward_train:1,feedback:4,feeddorward:4,feedforward:[1,4,12],feel:[0,5,6,11,13,15,19],feet:0,fetch:6,fetch_california_h:[],fetch_openml:[],few:[1,3,4,5,9,18],fewer:[0,9,11],ffnn:[1,12],field:[0,3,6,12,15],fifth:[0,6],fig:[0,1,2,3,4,6,7,12,13,14],fig_id:[0,6,7,9],figaxi:18,figsiz:[0,1,2,3,4,6,7,8,9,10],figur:[0,1,2,3,4,5,6,7,8,9,10,12,13,14,15],figure_id:[0,6,7,9],figurefil:[0,6,7,9],file:[0,1,2,4,5,6,7,9],file_prefix:4,filenam:[],filenotfounderror:[],fileout:[],filepath_or_buff:0,fill:[5,9],filter:[3,4],filtered_flat_arg:[3,4],filtered_tb:[3,4],financ:0,find:[0,1,2,3,5,6,7,8,9,10,11,12,13,14,15,18],find_top_boxed_arg:2,fine:[0,14],finish:2,finit:[3,5,6,12,13,18],first:[0,1,2,3,5,6,7,8,9,10,11,13,14,16,17,18,20],firsteigvector:11,fit:[1,3,4,5,6,7,8,9,11,12,13,18],fit_beta:6,fit_intercept:[0,5,6],fit_mod:9,fit_transform:[0,6,8,9,11],fiti:0,five:[0,9],fix:[0,3,4,6,10,11,12,13],fixedformatt:6,fixedloc:6,fkkt:5,flag:4,flat:[12,13],flatten:[1,3,4,5,16],flexibl:[1,6,8,10,12],float32:[4,9],float64:[4,16],flop:[5,16],flow:[1,4,12],fluctuat:5,fly:11,flyvbjerg:[],fm:0,fma:[],fmax:3,fmesh:13,fn:[3,4],focu:[0,3,4,5,6,15,20],focus:[1,6,7,16],fold:[6,9],folder:[0,4,6],follow:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20],font:[0,7,18],fontdict:18,fontsiz:[1,6,8,9,10,18],fontweight:1,footprint:3,foral:8,forc:[0,5,6,10,11],forcast:4,forecast:[4,12],forelesningsvideo:17,forest:[0,1,9,15,17],forget:11,form:[0,3,4,5,6,7,8,9,11,12,13,15,16,18],formal:[3,4,14,18],format:[0,1,2,3,4,6,7,8,9,10,11,15,18,20],format_data:4,formatstrformatt:[6,13],formul:[4,6,11,14],formula:[3,13,18],forth:[4,12],fortran2003:15,fortran90:18,fortran:[0,15,16],fortun:[0,11],forward:[0,3,6,15,16,17],forward_backward:[3,4],found:[1,2,4,5,6,12,13],foundat:15,four:[4,5,6,8,12,16,17],fourier:0,fourierdef1:3,fourierdef2:3,fourierseriessign:3,fourth:12,fr:5,frac:[0,1,2,3,5,6,7,8,9,10,11,12,13,14,16,18],fractal:[],fraction:9,frame:7,framework:[1,8,10,18],frank:[5,11],frankefunct:[5,6,11],free:[0,6,11,13,15,16,18,19,20],freecodecamp:15,freedom:5,freeli:0,frequenc:[3,6,7,18],frequent:[0,8,9,13],frequentist:15,fresh:10,frida:19,fridai:17,friedman:[6,17,20],friendli:4,frog:3,from:[0,1,2,3,4,6,7,8,9,11,13,14,15,16,17,18,19,20],from_cod:9,from_logit:[3,4],from_tensor_slic:4,fromnumer:2,front:[0,4,5],fstream:[],fulfil:[2,5,12],full:[0,1,3,5,7,9,10,13,18],full_matric:5,fulli:[3,6,12,17,18],fun:[2,13,15],fun_nam:[],func:[0,2],functionali:11,fundament:[0,6,15],funtion:2,further:[2,9],furthermor:[0,3,5,6,7,11,12,13,15],futur:[0,4,8,9],futurewarn:[],fx:5,fy:[17,19],g0:2,g42mrgv128v34gnnhxwk9nrc0000gp:[],g:[0,1,2,3,4,5,6,8,9,10,11,13,18],g_0:2,g_1:[2,10],g_2:[2,10],g_:[2,9,10],g_analyt:2,g_dnn_ag:2,g_euler:2,g_i:2,g_m:[3,10],g_n:3,g_re:2,g_t:2,g_t_d2t:2,g_t_d2x:2,g_t_dt:2,g_t_hessian:2,g_t_hessian_func:2,g_t_jacobian:2,g_t_jacobian_func:2,g_trial:2,g_trial_deep:2,g_vec:2,gain:[1,5,9,10,13],galleri:0,game:4,gamma1:8,gamma2:8,gamma:[0,2,8,9,10,11,13],gamma_0:10,gamma_1:10,gamma_1x:10,gamma_:0,gamma_i:[0,8,18],gamma_j:13,gamma_k:13,gamma_m:10,gamma_x:0,gap:[6,8],gate:[4,12],gather:[0,1,12],gaug:12,gaussbacksub:16,gaussian:[4,5,6,8,14,18],gaussian_point:14,gaussian_rbf:8,gave:13,gbc:17,gca:[2,6,8,13],gd:1,gd_clf:10,gdclassiffiercgain:10,gdclassiffierconfus:10,gdclassiffierroc:10,gdm:13,gdregress:10,ge:[1,5,7,18],gemv:5,gen:[],gen_loss:4,gen_tap:4,gender:0,genener:4,gener:[0,1,2,3,5,6,8,10,11,12,13,14,16,18,20],generallay:12,generate_and_save_imag:4,generate_imag:4,generate_latent_point:4,generate_simple_clustering_dataset:14,generated_imag:4,generator_loss:4,generator_loss_list:4,generator_model:4,generator_optim:4,genexpr:[],genom:15,geodes:11,geometr:[0,13],geometri:5,georg:20,geotif:6,geq:[2,5,8,9,13],geron:[0,17,20],get:[0,1,2,3,4,5,6,7,9,10,11,13,15,16,18],get_dummi:9,get_loc:[],get_next_color:[],get_paramet:2,get_split:9,get_yaxi:8,get_yticklabel:6,getsolutionslic:5,getval:[],gg:[],gibb:15,gif:4,gini:10,gini_index:9,git:[0,15],github:[0,3,4,6,14,15,17,20],gitlab:[0,15],give:[0,1,2,3,5,6,7,8,9,10,12,13,14,15,17,18,20],given:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],global:[6,7,13],glorot:1,gmail:[],go:[0,1,3,5,6,8,9,11,12,13],goal:[0,7,9],goe:[0,1,2,5,6,13,14,16],golden:13,gone:5,gong:1,good:[1,3,4,5,6,9,10,11,13,15,17,18,20],goodfellow:[4,17,20],googl:[1,3,4,14,15],got:[1,6],gov:6,gp:20,gpu:1,grad:[2,13],grad_analyt:13,grade:17,gradient:[0,3,4,7,8,9,12,15,17],gradient_desc:3,gradientboostingclassifi:10,gradientboostingregressor:10,gradients_of_discrimin:4,gradients_of_gener:4,gradienttap:4,gradual:[1,14],grai:[4,6],graph:[1,9,11,12,13],graph_from_dot_data:9,graph_funct:[3,4],graphic:[0,1,9],grasp:0,gray_r:[1,3],grayscal:3,great:[5,13],greater:[1,7,18],greatli:13,greedi:9,green:[0,3,9,18],grei:4,grid:[1,3,6,7,8,12,18],grossli:13,ground:0,group:[0,6,7,9,14,15,17],groupbi:0,grow:[1,3,9,10],growth:0,gru:4,guarante:[0,3,4,13,18],guess:[1,4,10,13,14],guestrin:10,guid:1,guilherm:19,h1:2,h21:17,h:[0,1,5,6,8,13,18,20],h_1:[2,13],h_2:[2,13],h_:[0,13],h_m:10,ha:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],habit:0,had:[0,1,6,7,13],hadamard:[1,12],half:[1,8,9],halv:10,hand:[0,1,2,3,5,11,12,13,15,16,17,18,20],handi:3,handl:[0,1,2,5,9,11,15],handle_unknown:9,handsid:12,handwrit:12,handwritten:[1,5],happen:[1,2,3,4,5,6,10,13,18],hard:[1,7,8,10,13],hardcopi:15,harder:[0,1],harmon:3,hasn:1,hassl:[0,15],hast:15,hasti:[0,6,17,20],hat:[1,5,6,7,9,10,11,12,13,16],have:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],haven:1,he:7,head:[0,4,10,18],header:0,heads_proba:10,health:0,hear:[0,13],heart:[0,7],heatmap:[0,1,3,7],heavili:0,heavisid:1,height:[1,3,6],held:13,help:[0,1,4,12,13],helper:[4,14],henc:[0,5,6,8,9,10,12,13],her:7,here:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20],hereaft:[0,8,12],hermitian:16,hessenberg:16,hessian:[0,2,5,13],heterogen:[9,10],hi:7,hidden:[1,3,4,12],hidden_bia:1,hidden_bias_gradi:1,hidden_layer_s:[0,1],hidden_neuron:4,hidden_weight:1,hidden_weights_gradi:1,hierarch:5,high:[0,1,2,3,4,5,6,9,10,11,13,14,15,16],higher:[0,1,3,5,6,8,13],highest:[1,2],highli:[0,3,4,10,15,16,20],highwai:0,hing:8,hint:13,hip:15,hire:0,hist:[4,6,7,18],histogram:[0,6,7,18],histor:[7,11],histori:[3,4,12],histplot:[],hit:[],hitherto:5,hjorth:19,hobbi:18,hoc:5,hoff:20,hold:[1,3,6,13,14],holder:0,home:0,homework:[6,13],homogen:[1,3,9,10,13],honchar:2,hopefulli:[0,11,18],horizont:11,hors:[3,7],hot:[1,9],hour:[1,15,17,18,19],house_pric:[],how:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,20],howev:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],hs:[],hspace:[0,4,8,10,18],hstack:1,htf:17,html:[7,11,15,17,20],http:[3,4,6,7,11,13,14,15,16,17,20],huang:0,huber:0,huge:[1,3,4,15],human:[0,1,3,6,9,12],humid:9,hundr:1,hungri:1,hybrid:17,hydrogen:0,hyperbol:[1,4,12],hyperparam:8,hyperparamet:[3,4,5,6,9,13],hyperplan:11,i0:0,i1:[0,6,8,12],i2:[0,8,12],i3:[0,12],i5:0,i:[0,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18,19],i_1:[5,6],i_2:[5,6],i_:13,ian:20,ic:1,id:[7,13],ida:19,idea:[0,1,2,3,4,6,9,10,12,13,16],ideal:[0,2,6,8,13,18],idem:6,ident:[5,6,12,13,16],identical:5,identifi:[0,1,7,9,11,12,13,14],idum:[],ieor:18,ifi:20,ifs:15,ignor:[0,1,3,9],ii:[16,18],iii:16,ij:[0,1,3,6,8,12,14,16,18],ik:[0,16],illustr:[5,7,10,12,13,14,15],im:6,imag:[1,3,4,6,9,11,12,14,20],image_at_epoch_:4,image_batch:4,image_height:3,image_path:[0,6,7,9],image_width:3,imageio:6,images_from_seed_imag:4,imagin:1,immedi:[0,3,4,6,15],implement:[0,2,3,4,5,6,8,9,10,11,12,13,14,18],impli:[3,5,6,7,13,16],implicit:3,implicitli:[11,18],importantli:3,impos:[0,6,11,12],imposs:[0,5],impress:[0,12],improv:[0,4,5,9,10,11,13],impur:9,imread:6,imshow:[1,3,4,6],in3050:20,in4080:20,in4300:20,in5400:[3,20],in_out_neuron:4,inaccur:13,inacio:19,inact:12,inadequ:0,inch:6,includ:[0,1,2,3,4,5,6,7,11,12,15,18,19,20],include_bia:[6,9],incom:12,incorrect:1,incoveni:8,increas:[0,1,3,4,5,6,7,8,9,11,12,13,18],increasingli:18,ind:6,inde:[0,2,4,5,6],indefinit:4,indent:[],indentationerror:[],independ:[0,5,6,7,8,12,13,18],index:[0,1,3,4,10,14,15,16,18,20],index_col:0,index_of:[],indic:[0,1,3,4,5,6,9,10,11,13],indispens:6,individu:[1,6,7,10,12,18],indu:0,indx1:2,indx2:2,indx3:2,indx:16,ineffici:[3,13],inequ:8,inequaltii:13,inertia:13,inf1000:15,inf1100:15,inf1100l:15,inf1110:15,inf3000:20,inf4490:20,inf5860:20,infeas:9,infer:[0,1,4,6,20],infer_nrow:0,inferenc:1,infil:[0,6,7,9],infin:[5,6,7,11],infinit:3,infinitesim:18,influenc:[6,10],influenti:1,inform:[0,1,3,4,6,9,11,12,13,14,16,20],infti:[3,6,13,18],ingeni:13,ingrad:2,ingredi:[0,9],inher:6,inherit:16,initi:[0,1,2,6,10,13,14,16,18],initial_epoch:[3,4],initialis:[],initialize_root:[],inject:14,inlin:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],inner:13,innov:20,inp:4,inplac:13,input:[0,1,3,4,5,6,7,8,9,10,12,13,14,18],input_dim:1,input_shap:[3,4],inputs:1,inputs_shuffl:[0,1],insert:[3,5,6,8,10,18],insid:[0,4,7],insight:[0,1,5,15,20],insist:[6,13],inspir:[0,1,12,20],instabl:2,instal:[0,1,5,6,9],instanc:[0,1,2,4,6,9,11,13],instanti:10,instead:[0,1,2,3,4,5,6,8,9,11,13,14,16,18],institut:1,instruct:[0,1],int32:10,int64:[],int_0:18,int_:[3,6,18],int_a:18,intak:0,integ:[1,2,13,14,16,18],integer_vector:1,integr:[3,6,18],intellig:[0,14,20],intend:10,intens:1,intention:14,interact:[0,6,9,12,15],intercept:[0,6,8,11,13],intercept_:[0,6,8,9,13],interchang:[5,12,16],interconnect:1,interest:[0,1,2,3,4,5,6,7,8,9,12,15,17,18],interfac:[0,1,16],interior:[0,9],intermedi:16,intern:[1,10,12],interpol:[1,3,4,6,12],interpr:5,interpret:[0,1,6,9,10,12,13,16,18],interv:[0,3,5,6,7,13,18],intial:13,intract:[0,4],intrins:[3,11,16,18],intro:[15,20],introduc:[0,1,5,6,8,10,12,13,16,18],introduct:[1,2,4,13,17,20],introductori:[0,4,16,20],intuit:[0,5,6,8,12,13],inv:[0,5,13],invalid:1,invalu:[0,13,15],invari:1,invd:5,inver:8,invers:[0,3,6,13],invers_period:[],inverse_transform:8,invert:[0,5,7,10],invok:[0,8],involv:[0,2,6,7,11,12],io:[0,15,17,20],iomanip:[],iostream:[],ip:[0,8,18],ipca:11,ipykernel_42331:[],ipykernel_42376:[],ipykernel_42449:[],ipykernel_42456:[],ipykernel_42530:[],ipykernel_42541:[],ipykernel_42553:[],ipykernel_42573:[],ipykernel_42580:[],ipykernel_42586:[],ipykernel_47411:[],ipykernel_47448:[],ipykernel_47647:[],ipykernel_47724:[],ipykernel_47735:[],ipynb:15,ipython:[0,1,2,3,4,5,6,7,8,9,10,11,13,14,15],iq:6,iri:[8,9],irreduc:6,irrelev:5,irrespect:0,is_integ:[],isbox:[2,13],iscomplexobj:[],isinst:[2,13],isn:5,isnul:0,isomap:11,issu:[1,3,4,9,14,16],it_arrai:13,item:[0,13],items:16,iter:[1,2,3,4,6,7,8,11,13,14,18],itr:5,its:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,20],itself:[5,6,12,18],ix:[],j1:16,j:[0,1,2,3,4,5,6,8,9,11,12,13,14,16,18,20],j_:6,j_lasso_sk:6,j_ridge_sk:6,j_sk:6,jackknavg:[],jackknif:[6,15],jackknstd:[],jackknvar:[],jackknvec:[],jacobian:[2,13],jacobian_shap:2,jargon:[],jason:4,jax:[3,4,14],jensen:19,jerom:20,ji:[12,16],jj:[0,5,6],jk:[0,1,6,12,16],jl:0,jm:16,joao:19,joaogca:19,job:[2,8,10],join:[0,4,6,7,9],joint:[4,5],journal:[],judg:13,judgement:6,julia:[15,16],jump:18,junk:4,jupyt:[0,15,20],just:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,18],justif:0,justifi:[3,10],jy:[],k0:7,k1:7,k:[0,1,3,5,6,7,8,9,10,11,12,13,14,15,16,18],kaggl:6,kappa_d:18,karim:[],karlsen:[],karush:8,keep:[0,1,4,5,6,11,13,14,16],keepdim:[1,6,10,16],kei:[0,1,3,6,12,20],kept:[4,6,14],kera:[0,4,15],kernel:[0,1,3,15],kernel_regular:[1,3],kernel_s:4,kernelpca:11,kev:0,kevin:20,keyboardinterrupt:[2,3,4],keyerror:[],keyword:[6,13,16],kfold:6,kg:1,ki:16,kick:[1,13],kiener:2,kilomet:6,kind:[0,2,3,4,8,12,13,14],kj:[6,12,16],kjm:15,kkt:[5,8],kktsolver:5,kl0m3:17,kl:18,km:12,kmean:14,kmeanspoint:14,kn_k:14,know:[0,1,2,5,6,8,13,15],knowledg:[0,15],known:[1,3,4,5,6,7,8,9,12,16,18,20],kondev:0,kp:18,kpca:11,kroneck:14,kuhn:8,kwarg:[0,2,3,4,13],kwd:[0,3,4],kwown:0,l0:7,l1:[0,1,3,5,7],l1_l2:[1,3],l1regl:5,l1regls_mosek2:5,l1regls_mosek:5,l2:[1,3],l:[0,1,2,3,5,6,7,8,10,11,12,13,16,18],l_1:7,l_2:[7,13],l_:16,l_j:12,la:13,la_i:12,la_k:12,lab:[15,17],label:[0,1,2,3,4,5,6,7,8,9,10,12,13,14,15,16,18],label_prob:13,labelencod:[7,10],labels:[6,8,9],labels_shuffl:[0,1],laboratori:17,lack:0,lagari:2,lagrang:[8,11],lambda:[0,1,2,3,5,6,7,8,10,12,13,18],lambda_0:11,lambda_1:[5,8,11],lambda_2:[8,11],lambda_:11,lambda_i:[8,11],lambda_iy_i:8,lambda_jy_iy_j:8,lambda_k:8,lambda_n:[5,8],lamda:1,lamdbda:5,land:[0,8],landmark:8,landscap:13,langl:[0,6,11,18],languag:[0,1,4,8,15,16,20],lapack:[5,16],laplac:5,laptop:15,larg:[0,1,2,4,5,6,8,9,10,11,13,15,16,18,20],larger:[0,3,5,6,8,10,11,13,18],largest:[4,8,11],lasso:[0,7,15,17],lasso_sk:6,last:[0,1,2,3,4,5,6,7,8,9,10,12,13,16,17,18],latent:4,latent_dim:4,latent_point:4,latent_space_value_rang:4,later:[0,1,4,6,7,8,12,13,14,15],latest:4,latest_checkpoint:4,latter:[0,3,5,6,7,8,11,13,16,17,18],lattic:12,law:0,layer:[0,4,13],lbfg:[7,9,10,11],lcc:[5,6],lda:11,ldot:[0,6,11],le:[5,7,10,13,18],lead:[0,1,3,5,6,7,8,9,10,11,12,13,16,18],leaf:9,leaki:1,leakyrelu:4,lear:13,learn:[3,4,5,6,7,8,9,10,12,16,17,20],learnabl:3,learner:10,learning_r:[3,8,10],learning_rate_init:[0,1],learning_schedul:13,least:[0,2,7,8,10,11,15,16,17,18],leat:13,leav:[0,1,3,5,6,9,11],lectur:[0,1,5,10,11,12,13,15,16,17,20],lecturenot:[15,20],lecturenovember11:[],lecturenovember12:[],lecturenovember19:[],lecturenovember25:[],lecturenovember26:[],lecturenovember4:[],lecturenovember5:[],lectureoctober14:17,lectureoctober15:17,lectureoctober1:[],lectureoctober21:[],lectureoctober22:[],lectureoctober28:[],lectureoctober29:[],lectureoctober7:[],lectureoctober8:[],lectureseptember10:[],lectureseptember16firstpart:[],lectureseptember16secondpart:[],lectureseptember17:[],lectureseptember23:[],lectureseptember24:[],lectureseptember2:[],lectureseptember30:[],lectureseptember3:[],lectureseptember9:[],lecturethursdayaugust26:[],lecturethursdayaugust27:[],left:[0,1,2,3,5,6,7,8,9,10,11,12,13,14,16,18],leftarrow:[8,12],legend:[0,2,3,4,5,6,7,8,9,10,13],len:[0,1,2,3,4,5,6,8,9,10,11,12,16],len_index:0,length:[0,1,2,3,4,8,9,13,15],length_of_sequ:4,leq:[0,5,7,8,13,14,18],less:[0,1,3,4,5,6,8,9,13,18],lessen:1,let:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],letter:[0,16,18],level:[0,1,5,6,9,15,16,17],li:[8,11],lib:[0,1,2,3,4,6,7,8,11,13,14],liblinear:[8,10],librari:[0,1,2,3,4,5,6,9,10,11,16,18,20],licens:[0,1,15],lie:[0,6,11,18],life:[0,1,8,12],lifetim:13,like:[0,1,2,3,4,5,6,7,9,10,11,12,13,15,16,18],likelihood:[0,1,5,9,13],lim_:18,limit:[0,5,6,7,8,11,12,16],lin_clf:8,lin_model:0,lin_reg:9,linalg:[0,2,5,6,8,11,13,16,18],line1:8,line2:8,line2d:13,line3:8,line:[0,1,3,6,8,11,13],linear:[1,3,5,6,7,9,10,11,12,15,17,18],linear_model:[0,5,6,7,8,9,10,11,13],linear_regress:6,linearli:5,linearloc:[6,13],linearregress:[0,6,7,9],linearsvc:8,liner:[1,3],linerar:10,linewidth:[0,2,4,6,8,9,10],link:[0,4,9,12,15],linlag:5,linpack:16,linreg:0,linspac:[0,2,3,4,6,8,9,10,13,16,18],linu:4,linuek:[],linux:[0,1,15],liquid:0,list:[0,1,2,3,4,5,9,15],listcomp:2,listedcolormap:[9,10],literatur:[1,7,14,20],littl:[1,3,9,12],live:8,ll:[0,18],lle:0,lloyd:[4,14],lmb:[2,5,6],lmbd:[0,1,3],lmbd_val:[0,1,3],lmbda:13,ln:[1,13],lo:5,load:[0,1,4,6,7,9,10],load_boston:0,load_breast_canc:[1,7,9,10,11],load_data:[3,4],load_digit:[1,3],load_iri:[8,9],loc:[0,3,6,7,8,9,10],local:[0,1,3,7,12,13],locat:[2,3,8],lock:[3,4],log10:[5,6],log:[0,1,2,4,5,6,7,9,10,11,13,16],log_:0,log_clf:10,logarithm:[0,5,7,16],logic:[0,1,9],logist:[0,1,2,8,9,10,11,12,15,17],logistic_predict:13,logisticregress:[7,9,10,11],logit:7,logreg:[7,9,10,11],logspac:[0,1,3,5,6],longer:[2,3,8,10,14,16,18],loocv:6,look:[0,1,2,3,4,5,6,7,8,9,10,11,13,16,18],loop:[1,4,6,10,12,14,15,16],lose:1,loss:[0,1,3,4,5,6,7,8,10,11,13,16],loss_fil:4,lossfil:4,lost:4,lot:[0,1,4,6],low:[0,6,9,10,11],lower:[0,1,3,6,9,10,16],lowercas:16,lowest:[9,13,18],lr:[1,3,4,10],lstat:0,lstm:4,lstm_2layer:4,lstsq:0,lt:6,lu:[0,5],lubksb:16,luckili:2,ludcmp:16,lux:16,lvert:1,lw:0,m:[0,1,2,3,5,6,8,9,10,11,12,13,16,17,18,19,20],m_1:14,m_:[9,12],m_h:0,m_k:14,m_l:12,m_n:0,m_p:0,m_t:13,ma:11,mac:[3,4,14],machin:[1,3,4,5,6,7,9,10,11,12,14,16,17,20],machinelearn:[6,15,17,20],mackai:20,made:[0,1,3,4,5,6,7,9,11,12],mae:0,magic:4,magnitud:[1,6,7,13],mai:[0,1,2,3,5,6,7,8,9,11,12,13,15,16,18],mail:17,main:[0,1,3,4,5,6,7,9,16,20],mainli:[0,5,6,7,9],maintain:6,major:[1,6,9,10,13,16],make:[1,2,3,4,5,6,7,8,11,12,13,15,16,18,20],make_axes_locat:6,make_moon:[8,9,10],make_pipelin:[0,6,10],make_vjp:[2,13],makedir:[0,6,7,9],makeplot:0,malcondit:16,malign:[1,7,9],mammographi:5,manag:[0,2,3,15],mani:[0,1,3,4,5,6,7,8,9,11,13,14,15,16,18,20],manifold:11,manner:3,manual:6,map:[0,1,2,6,7,8,11,12,14,18],margin:[0,5,8],mari:19,marit:0,marker:[0,7,16],markov:15,marsaglia:18,mask:[],mass:[0,1,5,13],massag:0,masses2016:0,masses2016ol:0,masses2016tre:0,masseval2016:0,master:[6,17],mat1100:15,mat1110:15,mat1120:15,mat3155:[],mat4155:[],mat:15,match:[0,1,4,5,13,14],materi:[4,5,7,13,16],math:[3,7,12,13,16,18,20],mathbb:[0,4,5,6,7,8,11,12,13,14,16,18],mathbf:[0,5,6,7,8,13,16],mathcal:[1,5,6,7,13],matheemat:3,mathemat:[0,6,11,12,13,15,16,17,18,20],mathrm:[0,1,3,4,5,6,7,8,9,10,11,12,13,14,18],matmul:[1,2,5],matmul_adjoint_1:2,matmul_vjp_0:2,matmul_vjp_1:2,matnat:[17,19,20],matplotlib:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],matplotlibdeprecationwarn:[6,13],matric:[0,1,3,4,6,7,8,11,13,15],matrix:[0,2,3,4,6,7,8,10,13,18],matshow:1,matter:[2,3,13],max:[0,1,2,3,4,9,10,12,13],max_depth:[0,9,10],max_diff1:2,max_diff2:2,max_diff:2,max_it:[0,1,7,8,11,13],max_iter:14,max_leaf_nod:10,max_queue_s:[3,4],max_sampl:10,maxdegre:[0,6,10],maxdepth:10,maxim:[1,4,5,7,8,11],maximum:[0,1,2,3,5,7,8,9,10,13,14],maxpolydegre:[5,6],maxpooling2d:3,mbox:[5,6],mc:[],mcculloch:12,mcint:[],mcintsqr2:[],md:11,mdoel:4,mean:[1,2,3,4,5,6,7,9,10,11,12,13,14,15,16,18],mean_absolute_error:0,mean_divisor:14,mean_i:18,mean_matrix:14,mean_squared_error:[0,4,6,7,10],mean_squared_log_error:0,mean_vector:14,mean_x:18,meaning:[0,4,7],meansquarederror:0,meant:[2,3,7,10,13],meantempvec:[],meanvec:[],measur:[0,1,2,5,6,9,11,12,14,18],mechan:[0,4,18],median:0,medicin:12,medium:[4,8,13],medv:0,meet:[0,19],mehta:0,memori:[3,4,11,12,13,16],mention:[0,12,13,18],mere:0,mersienn:[],meshgrid:[2,5,6,8,9,10,11],messag:[5,13],messi:2,met:[0,3,8],metadata:2,meteorolog:9,meter:6,method:[0,1,2,3,4,5,7,8,11,12,14,15,16,17,18,20],metion:6,metric:[0,1,3,6,7,9,10,14],metropoli:15,mev:[0,18],mgd:13,mglearn:15,mgrid:13,mhjensen:[1,2,3,4,6,7,8,11,14],mi:10,microsoft:20,mid:1,midel:4,midpoint:9,might:[0,1,2,4,6,9,13],mild:9,miller:[],millimet:6,million:0,mimic:12,min:[0,2,5,8,9],min_:[0,2,5,14],min_samples_leaf:9,mind:[0,6,13],mindboard:4,mine:15,mini:[1,11,12,13],minibatch:[1,11,13],minibathc:13,miniforge3:[0,1,2,3,4,6,7,8,11,13,14],minim:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],minima:[0,1,7,13],minimum:[0,1,2,6,8,9,11,13],minmaxscal:0,minor:[6,13,18],minst:1,minu:7,mirror:9,misc:6,misclassif:[8,9,10],misclassifi:[8,10],miser:0,mismatch:1,miss:[0,10],mistak:4,mit:20,mix:[1,2],mixtur:13,mk:[9,16],mkdir:[0,6,7,9],ml:[0,1,10,13,16],mlab:18,mle:[5,7],mline:[],mlir:[],mlir_graph_optimization_pass:[],mlp:1,mlpclassifi:1,mlpregressor:0,mm:16,mn:[12,18,20],mnist:[1,11],mod:18,mode:17,model:[2,3,5,7,8,9,10,11,13,14,15,18,20],model_select:[0,1,3,5,6,7,9,10,11],moder:10,modern:[0,6,7,15],modif:[2,12,13],modifi:[0,1,3,5,7,8,10,12,13],modul:[0,2,3,4,5,6,7,8,9,10,11,13,16],modular:18,modulenotfounderror:[5,7,8,9],modulo:18,moe:[5,11],moment:[5,6,13,18],monitor:13,monoton:[5,12,18],mont:[0,6,15,18,20],montecarlocycl:[],moor:[5,6],more:[0,1,2,4,5,7,8,9,10,11,12,13,14,15,17,18],moreov:[0,3],morten:19,mortenimac:[],mosek:5,most:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,17,18],mostli:[1,11],motion:[0,13],motiv:[1,4],move:[0,4,5,6,7,9,12,13,14,18],mp4:17,mpl:[0,7],mpl_toolkit:[2,6,13],mplot3d:[2,6,13],mplregressor:1,mse:[0,4,5,6,9,10],mse_simpletre:10,mselassopredict:5,mselassotrain:5,mseownridgepredict:6,msepredict:5,mseridgepredict:[5,6],msetrain:5,msg:[],msle:0,mt19937_64:[],mt:[7,12],mu0:18,mu1:18,mu2:18,mu:[0,6,11,13,18],mu_:[6,18],mu_i:6,mu_n:11,mu_x:18,much:[0,1,2,3,4,5,6,8,9,10,11,12,13,16,18],mul:5,multi:[0,1,3,7,15],multiclass:[1,7],multidimension:[11,12],multilay:1,multinomi:7,multipl:[2,4,5,6,7,12,13,18],multipli:[3,5,6,11,13,16,18],multiplum:8,multivari:[0,2,10,11,15,18],multivariate_norm:[11,14],murphi:[11,20],must:[0,1,2,5,6,8,10,12,13,14,18],mutat:7,mutual:[1,3,6,13],mx_:18,myenv:[0,1,2,3,4,6,7,8,11,13,14],myriad:[0,15],mz1:18,mz2:18,n1:16,n2:16,n:[0,1,2,3,4,5,6,7,8,10,11,12,13,14,16,18],n_0:[12,18],n_:[1,2,3,8,12,18],n_b:[],n_boostrap:[6,10],n_bootstrap:6,n_categori:[1,3],n_cluster:14,n_compon:11,n_epoch:13,n_estim:10,n_examples_to_gener:4,n_featur:1,n_filter:3,n_hidden:2,n_hidden_neuron:[0,1],n_i:18,n_input:[0,1,3],n_instanc:9,n_iter_i:[7,11],n_job:10,n_k:14,n_l:[12,18],n_layer:1,n_m:9,n_neuron:1,n_neurons_connect:3,n_neurons_layer1:1,n_neurons_layer2:1,n_point:14,n_sampl:[6,8,9,10,14],n_split:6,n_step:4,n_t:2,n_x:2,nabla:[1,13],nabla_:[2,13],nabla_w:13,nag:13,naimi:0,naiv:7,naive_kmean:14,nall:0,name:[0,1,3,4,5,6,7,8,9,10,12,13,14,15,16,18,19],nameerror:[6,10],namespac:[],narrow:13,nary_f:[2,13],nary_op_arg:[2,13],nary_op_kwarg:[2,13],nary_oper:[2,13],nation:[1,5],nativ:15,natur:[0,1,4,8,9,12,13,18,20],navier:12,nb:18,nb_:16,nboot:[],nd:14,ndarrai:[2,6],ndim:2,ne:[9,10,16,18],ne_xcl2ctm0:17,nearest:[1,3,6,11],nearli:13,neccesari:6,necess:2,necessari:[0,1,3,4,8,14],necessarili:[0,4,11,18],necesserali:5,neck:7,need:[0,1,2,3,4,5,6,8,9,10,11,12,13,14,16,18],neg:[0,1,3,5,6,7,10,13,16,18],neg_mean_squared_error:6,neglect:18,neglig:18,neighbor:[3,6,11],neither:[4,13],neq:[13,14,18],nervou:12,nest:[2,9,12],nesterov:13,net:[2,4,12],netlib:16,network:[0,9,13,15,17,20],neural:[0,7,13,15,17,20],neural_network:[0,1,2],neuralnetwork:1,neuron:[1,2,3,4,12],neutral:0,neutron:0,never:[1,3,4,6,9,18],new_box:[2,13],new_root:[2,13],new_trac:[2,13],new_tracing_count:[3,4],newaxi:[0,3,6,9],newli:0,newton:[1,7,8,13,18],next:[0,1,2,3,4,5,6,8,9,13,14],next_guess:13,next_input:4,ng:1,ngini:[],ni:14,nian:[],nice:[0,1,5,11],nichola:[],nicholaskarlsen1102:[],niter:13,nitric:0,nlambda:[5,6],nlevel:[],nm:18,nm_n:0,nmse:6,nn:[2,5,6,12,16],nn_model:1,nnmin:2,node:[1,2,3,9,10,12],node_constructor:2,nois:[0,4,5,6,8,9,10,13],noise_dimens:4,noisi:[1,6],non:[0,1,3,4,5,6,7,9,10,11,12,13,14,16,18],none:[0,1,2,3,4,5,9,10,13,18],nonlinear:[3,6,8,9,11,12],nonneg:[6,9,13],nonparametr:6,nonsens:18,nonsingular:16,nonumb:[3,7,8,13,16],nor:[1,4,13],norm:[0,1,5,6,8,11,13],normal:[3,4,5,6,7,8,9,10,11,12,13,15,16,18],normali:16,normalize_kwarg:[],norwai:6,notat:[0,2,5,6,13,14,18],note:[0,1,2,3,4,5,6,7,8,11,12,13,14,15,16,17,18,20],notebook:[0,1,3,9,15],noth:[1,2,5,8,12,14,18],notic:[4,5,12,13,16,18],notimplementederror:[],notion:3,notrace_primit:[],novel:[3,6,10],novemb:1,now:[0,2,4,5,6,7,8,10,11,12,13,14,16,18],nowadai:[0,1,3,9,15],nox:0,np:[0,1,2,3,4,5,6,7,8,9,10,11,13,14,16,18],npr:2,nsampl:6,nspin:[],nt:2,nthi:0,ntrained_model:6,nu:18,nuclear:5,nuclei:[0,18],nucleon:0,nucleu:0,num:4,num_coordin:2,num_hidden_neuron:2,num_it:2,num_neuron:2,num_neurons_hidden:2,num_output:[3,4],num_point:2,num_tre:10,num_valu:2,number:[1,3,4,5,6,7,8,9,10,11,12,13,14,16,17,19],numberid:7,numberparamet:3,numer:[0,5,6,9,10,11,12,13,15,16,20],numpi:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,18],numpy_vjp:2,numpy_wrapp:2,nunmpi:5,nvalu:[],nx:2,nx_test:6,nx_train:6,nx_train_mean:6,ny:18,ny_pr:6,ny_train:6,ny_train_mean:6,o:[0,6,7,8,9,11,16,20],obei:[6,11,13],object:[0,1,2,4,5,6,8,10,13,16],objsens:5,obliqu:5,observ:[1,3,5,6,7,8,9,10,11,12,13,14,18],obtain:[0,1,5,6,7,8,9,10,12,13,14,16,18],obviou:[5,6,11,18],obviouli:0,obvious:[0,4,5,6,16],oc:5,occupi:0,occur:[0,6,8,9,16,18],od:0,odd:[0,3,7],odenum:2,odesi:2,oen:0,off:[1,3,4,5,9,13,18],offer:[6,11,15,16,17],offic:19,offici:17,ofil:[],ofstream:[],often:[0,1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],ofter:16,ol:0,old:[1,5,10,13],ols_sk:6,ols_svd:6,olsbeta:5,omega:[2,3,6],omega_0:3,omit:[0,5],on_train_batch_begin:[3,4],onc:[1,6,9,11,13],one:[0,1,3,4,5,6,7,8,9,10,11,13,14,15,16,18],oneapi:[],onednn:[],onehot:1,onehot_vector:1,onehotencod:9,ones:[0,2,5,6,8,9,10,11,13,16,17],ones_lik:4,onl:3,onli:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],onlin:[11,17],onto:[5,11],op_nam:[3,4],open:[0,1,4,6,7,9,15,17],oper:[0,1,3,5,6,10,11,12,13,15,18],operation:18,oplu:18,opmiz:13,opportun:0,oppos:[6,13],opposit:[1,5,8],opt:[1,5],optim:[0,2,3,4,5,6,7,9,10,11,14,17],optimis:[1,3],optimizer_v2:3,option:[0,1,3,5,6,7,8,11,16],optionalxlacontext:[3,4],optmiz:[1,8,13],orang:0,order:[0,1,2,3,5,6,7,8,9,10,11,12,13,16,18],ordinari:[0,2,3,7,11,13,15,17],oreilli:20,org:[3,4,7,11,15,16,20],organ:[6,7,10,16],orient:[1,5,18],origin:[0,3,5,6,8,11,12,13,16],orthogn:5,orthogon:[0,5,6,8,11,13,16],orthonorm:5,os:[0,1,4,5,6,7,8,9],oscar:1,oscil:[3,13],oslo:[0,15,17,19],osx:[0,15],other:[0,1,2,3,5,6,7,8,10,13,14,15,17,18,20],otherwis:[0,1,4,7,13,16],ouput:[5,7,12],our:[1,2,3,6,7,8,9,10,12,14,15,16,17,18],ourmodel:0,ourselv:[0,5,6,8,11,13],out:[0,1,2,4,5,6,7,8,9,10,11,12,13,15,16,18],out_fil:9,outcom:[0,7,9,10,12,18],outdoor:9,outer:[6,12],outfil:4,outfilenam:[],outgrad:2,outlier:[0,8],outlin:[6,10,11],outlook:9,outperform:10,output:[0,1,3,4,5,6,7,8,9,10,12,13,16,18],output_bia:1,output_bias_gradi:1,output_shap:4,output_weight:1,output_weights_gradi:1,outputlayer1:12,outputlayer2:12,outsid:4,over1:13,over:[0,1,3,4,5,6,9,10,12,13,16],overal:[1,10],overcast:9,overcom:[12,13],overdetermin:0,overfit:[0,1,3,6,9,10,13],overflow:[1,5],overhead:12,overlap:[3,7,8,9],overlin:[0,5,6,9,10,11,14,16],overst:0,overtrain:4,overview:[3,20],own:[4,5,6,8,12,13,15,16],owner:0,ownridgebeta:6,oxid:0,oyvinssc:19,p0:2,p1:2,p:[0,1,2,3,4,5,6,7,8,9,10,11,13,14,16,18],p_:[2,4,8,9],p_hidden:2,p_i:[5,18],p_j:18,p_n:18,p_output:2,p_x:18,pack:0,packag:[0,1,2,3,4,5,6,7,8,11,13,14,15,18],pad:[3,4],page:[0,15],pai:[0,1,9,13],pair:[0,2,3,9,15,18],panda:[0,4,5,6,7,9,11,15],paper:1,paradigm:0,parallel:[10,16],param:[2,4],paramat:2,paramet:[0,1,2,3,4,5,6,7,8,9,10,12,13,18],parameter:[0,6,10],parametr:[0,6],paramt:[3,5],parent:2,parent_argnum:[],park:[],parser:0,part:[0,1,3,5,6,10,16,17,18,20],partial:[0,1,5,6,7,8,10,11,12,13,18],particip:[15,17],particl:[0,4,13,18],particular:[0,1,2,3,5,6,9,10,11,12,13,18,20],particularli:[5,6,8,11,13,18],partit:[1,4,9],partli:6,pass:[2,3,12,14],past:[10,18],patch:[6,18],path:[0,4,6,7,9,15],patient:7,patter:4,pattern:[0,3,4,12,17,20],pauli:0,pc:[11,15],pca:[0,7,15,17],pcolor:6,pcolormesh:6,pd:[0,4,5,6,7,9,11],pde:2,pdf:[0,3,4,5,6,9,20],pedagog:0,penal:6,penalti:[6,13],penros:[5,6],pentagon:13,peopl:[0,1,9,13,15],per:[0,1,6,17],percentag:[0,10,11],perceptron:[0,1,7],perfect:[0,1],perfectli:[4,6],perform:[0,2,3,4,5,6,8,10,11,12,13,14,15,16,18],performac:4,perhap:[0,5,13],perimet:1,period:[1,4,18],permut:11,persist:13,person:[5,6,7,17,19],perspect:20,pertin:12,petal:[8,9],peter:20,petersen:[],phantom:18,phase:[6,12],phenomena:18,phi:8,phi_k:8,philip:[],philosophi:13,phone:19,photo:4,phrase:0,physic:[0,1,4,7,12,13,18,19,20],pi:[2,3,5,6,7,9,12,13,18],pick:[1,9,10,11,13,14],pickl:1,pictur:0,pie:15,piec:[11,14],pillow:[0,15],pinv:[5,6,13],pip3:[0,1],pip:[0,1,15],pipelin:[0,6,8,10],pit:4,pitfal:6,pitt:12,pixel:[1,3,4],pixel_height:[1,3],pixel_width:[1,3],place:[0,4,6,8,13,16],plai:[0,3,4,5,6,8,11,15],plain:[8,10,12,13,14],plan:[6,9,19,20],plane:[8,9],plateau:5,platform:15,plausibl:12,pleas:[3,4,6,7,11,13,14],plenti:1,plethora:[3,12],plot:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],plot_confusion_matrix:[7,10],plot_count:6,plot_cumulative_gain:[7,10],plot_data:1,plot_dataset:8,plot_decision_boundari:[9,10],plot_import:10,plot_max:4,plot_min:4,plot_model:4,plot_numb:4,plot_predict:8,plot_regression_predict:9,plot_result:4,plot_roc:[7,10],plot_surfac:[2,6,13],plot_train:9,plot_tre:[9,10],plt:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],plu:[0,3,5,7],pm:8,pmatrix:2,pn:3,png:[0,4,6,7,9],point:[0,1,2,3,5,6,7,8,9,10,11,13,14,16,18,19],point_1:4,point_2:4,poisson:[15,18],poli:[6,8],poly100_kernel_svm_clf:8,poly3:0,poly3_plot:0,poly3dcollect:13,poly_featur:[8,9],poly_features10:9,poly_fit10:9,poly_fit:9,poly_kernel_svm_clf:8,polydegre:[0,5,6,10],polygon:13,polym:12,polynomi:[0,5,6,7,8,9,10,11],polynomial_featur:6,polynomial_svm_clf:8,polynomialfeatur:[0,6,8,9],polytrop:[0,6],pool:3,pool_siz:3,poor:[1,13],poorli:0,pop:2,popul:[0,5],popular:[0,1,3,6,7,8,9,11,12,15,16,18],popularli:0,portabl:10,portion:[11,13],pose:[0,4,5,6,11,18],posit:[0,1,2,3,5,7,8,10,11,13,14,16,18],possibl:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,18,19],posterior:5,postpon:0,postul:5,potenti:[0,3,5,6,12,13],potr:5,potrf:5,pott:12,power:[0,1,5,6,8,9,12,13],pp:[5,6],practic:[0,5,6,7,8,17,18],practition:[0,1,3],preced:[1,11,12,18],preceed:4,preceq:8,precis:[0,2,5,11,13,16,18],pred:[6,13],predicit:0,predict:[0,1,5,6,7,8,9,10,15,20],predict_prob:1,predict_proba:[7,10],predictor:[0,5,6,7,9,10,11],prefer:[0,1,6,8,9,11,15],prepar:[0,6,16],preprocess:[0,4,6,7,8,9,10,11],prerequisit:0,presenc:13,present:[0,5,6,9,12,13,16,17,18],preserv:[3,11,16],press:[13,20],pretrain:[1,4],pretti:[0,4,8,9,15],prev_centroid:14,prevent:[13,18],previou:[0,1,2,3,4,5,6,8,10,11,12,13,16,18],previous:[2,3,9,10,18],price:[0,4,9,13],primal:8,primari:[0,7],prime:18,primit:2,princip:[0,5,7,15,17],principl:[0,6,7,8,14],print:[0,1,2,3,4,5,6,7,8,9,10,11,13,14,16,18],print_funct:[8,9],printout:0,prior:[0,5,6],privat:0,prob:[1,18],probabilist:[0,20],probabl:[0,1,3,4,6,7,10,13,15,17],problem:[0,3,4,5,6,7,8,9,10,11,12,14,15,16,17,18],proce:[0,5,6,8,9,10,11,13,16],procedur:[2,4,5,6,8,10,11,13],proceed:16,process:[0,2,4,6,9,10,12,13,15,16,18,20],prod:20,prod_:[1,5,7],produc:[0,3,4,5,6,9,10,11,12,13,15,16,18],product:[0,1,3,5,6,7,8,12,13,15,16],profess:0,program:[0,1,4,5,6,8,12,14,15,16,17,18],programm:16,progress:[1,4,14],prohibit:6,project1:6,project:[0,1,2,3,5,11,13,15,17,19],project_root_dir:[0,6,7,9],promin:12,promis:8,prone:9,pronounc:[13,15],proof:[0,11,12,13],propag:[2,3,13,17],proper:[0,2,6],properli:[1,6,8,10,13],properti:[0,1,3,12,13,16],proport:[0,1,5,9,11,13,18],propos:[1,4,6,10],propto:[5,13],protect:[3,4],proton:0,prove:[3,13],provid:[0,1,3,4,5,6,8,9,10,12,13,15,16,18,20],proxi:[1,13],prune:9,pseudo:[16,18],pseudoinv:5,pseudoinvers:[5,6],pseudorandom:[6,18],psycholog:0,pt:13,ptratio:[],punish:[0,1],pure:[3,9,18],purest:9,puriti:9,purpos:[0,3,10,12,14],put:1,putarow:5,putboundslic:5,putclist:5,putobjsens:5,putqobj:5,py:[0,1,2,3,4,5,6,7,8,11,13,14],pydata:15,pydot:9,pylab:[0,7],pylint:[3,4],pypi:15,pyplot:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],pythagora:5,python2:0,python3:[0,1,2,3,4,6,7,8,11,13,14,15],python:[1,2,3,4,5,6,8,11,12,13,14,17,18],pytorch:[0,15],pywrap_tf:[3,4],q:[5,6,8,11,18],qp:8,qquad:[2,11,13,16],qr:[5,6,16],quad:[1,13,16],quadrat:[0,5,8,9,13],qualit:[4,9,18],qualiti:[0,9,15],quantifi:1,quantil:10,quantit:[0,6,9],quantiti:[0,2,5,6,7,9,10,11,12,14,16,18],quantum:[4,12],quartil:0,quench:5,queri:9,question:[0,5,6,9,11,12,13],qugan:4,quick:[4,18],quick_execut:[3,4],quickli:[1,3,9,11,13],quit:[1,5,6,9,10,12],quot:4,r2:[0,5,6],r2_score:0,r2score:0,r:[0,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],r_1:9,r_2:9,r_j:9,r_m:9,rad:0,radial:[0,8,12],radioact:18,radiu:[0,1],rag:2,rain:9,rais:[0,2,13],ramp:1,ran0:18,ran1:18,ran2:18,ran3:18,rand:[0,4,5,6,9,10,13,16],rand_max:[],randint:[6,9,13],randn:[0,1,2,5,6,9,11,13],random:[0,1,2,3,4,5,6,8,9,13,14,15,16,17],random_devic:[],random_forest_model:10,random_index:13,random_indic:[1,3],random_st:[0,7,8,9,10,11],randomforestclassifi:10,randomli:[1,6,9,13,14],randomnumbergener:[],rang:[0,1,2,3,4,5,6,7,9,10,11,12,13,14,16,18],rangl:[0,6,11,18],rangle_x:18,rank:5,rankdir:4,raphson:[1,8,13],rapidli:0,rare:[1,13],rate:[0,1,2,3,4,8,9,10,12,13],rather:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,16,18],ratio:[4,7,9,10,11],rational:0,ravel:[5,6,7,8,9,10,11,13,16],raw:3,raw_df:[],rbf:[8,11,12],rbf_kernel_svm_clf:8,rbf_pca:11,rc:[0,18],rcond:0,rcparam:[0,1,3,7,8,9,10,18],rd:[],re:[2,4,13],reach:[1,4,5,6,7,9,10,11,12,13,14],read:[0,2,3,4,5,6,7,8,11,12,16,17,18,20],read_csv:[0,6,7,9],read_fwf:0,reader:[0,6,16,18],readi:[0,1,5,6,8,10,11,12,16],readili:1,real:[0,1,2,4,7,10,11,12,13,16],real_loss:4,real_output:4,realist:8,realiti:18,realiz:[1,12],realli:[0,1],rearrang:13,reason:[0,1,3,4,10,13,20],reassign:1,rebuild:[],recal:[5,6,9,10,11,12,16,18],recalcul:[],recast:3,receiv:[1,3,10,12,18],recent:[0,2,3,4,5,6,7,8,9,10,13],recept:[3,12],receptive_field:3,recip:[0,6,7,16],reciproc:5,recogn:[0,4,5,10],recognit:[0,1,3,12,17,20],recommend:[0,2,3,4,5,6,8,13,15,16,17,20],reconsid:9,reconstruct:11,record:[10,17],recreat:[],rectangl:[9,13],rectangular:5,rectifi:[1,3,12],recur:[0,15],recurr:[0,1,15,17],recurs:[9,15,16],recycl:[],red:[0,3,4,6,8,9],redefin:[0,10],reduc:[1,3,5,6,9,10,11,13],reduct:[0,10,11,15,18],refer:[0,1,2,3,5,6,7,11,12,13,14,16,20],referenc:2,refin:12,refit:6,reflect:[0,1,4,5,18],refresh:[15,17],reg:[10,11],regard:[1,9,13],regardless:12,region:[3,4,6,9,12],regist:[6,18],reglasso:5,regr_1:[0,9],regr_2:[0,9],regr_3:[0,9],regress:[1,8,11,12,15,16,17],regressor:[0,7,10],regridg:[5,6],regular:[0,3,4,5,6,7,9,13],regularis:6,reilli:[0,20],reinforc:[0,8,15],reiter:1,rel:[0,4,6,7,9,12,13,18],relat:[0,1,3,4,5,11,13,14,16,18],relationship:[0,4,9],relativeerror:0,releas:[1,3,4,6,13,15],relev:[0,1,5,7,11,15,17,18],reli:[0,6,8],reliabl:[7,18],relu:[3,4],remain:[1,2,4,6,12,16,18],remaind:18,reman:2,remark:1,rememb:[0,8,13,16],remind:[0,5,11,13,16,18],remov:[0,4,5,6],render:0,reorder:[5,7],reorgan:0,repeat:[0,1,3,4,5,6,9,10,11,13,14,16,18],repeated:0,repeatedli:[6,10,13],repet:3,repetit:[6,17],rephras:13,replac:[0,1,3,4,5,6,10,12,13,14],replica:6,repositori:[0,4],repres:[0,1,2,3,4,5,6,7,8,9,10,12,13,18],represent:[0,1,3,6,18],representd:3,reproduc:[0,5,6,9,12,15,18],repuls:0,request:[0,13],requir:[0,1,3,4,5,6,8,9,11,12,13,16],res1:2,res2:2,res3:2,res_analyt:2,res_analytical1:2,res_analytical2:2,res_analytical3:2,resaml:6,resampl:[0,7,10,15,17],rescal:[0,11,12],rescu:5,reseach:6,research:[0,4,15,20],resembl:[6,18],reserv:[1,5,6,18],reset:[],reshap:[0,1,2,3,4,6,8,9,10,16],residenti:0,residu:[0,5,13],resiz:5,respect:[0,1,2,3,5,6,7,8,10,11,12,13,14,18],respond:12,respons:[0,7,9,12],rest:[0,5],restat:[0,12],restor:4,restored_discrimin:4,restored_gener:4,restrict:[0,3,9,12],result:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],result_typ:[],retail:0,retain:[5,6],return_data:14,return_kwarg:[],return_sequ:4,return_x_i:9,reus:[1,3,6],reveal:[0,12],revers:[1,16],review:[15,16,17],revisit:14,reward:[0,4],rewrit:[0,3,5,6,7,8,10,11,12,13,16,18],rewritten:[2,6,8,10,18],rewrot:13,rf:10,rgb:3,rgoj5yh7evk:15,rh:[5,6],rho:[0,10],rho_1:10,rho_2:10,rho_m:10,rich:0,ride:9,rideclass:9,ridedata:9,ridg:[0,7,11,13,15,17],ridge_sk:6,ridgebeta:5,right:[0,1,2,3,5,6,7,8,9,10,12,13,14,16,18],right_sid:2,rightarrow:[0,1,5,6,8,11,12,13,18],rigor:0,ring:6,rise:0,risk:[0,13],rival:4,river:0,rm:[0,18],rmse:0,rmsporp:13,rmsprop:[1,3,4,13],rnd_clf:10,rng:18,rnn1:4,rnn2:4,rnn:[4,12,17],rnn_2layer:4,rnn_input:4,rnn_output:4,rnn_train:4,rntrick1:18,rntrick2:18,rntrick3:18,rntrick4:18,ro:[0,13],robert:20,robust:0,robustscal:0,roc:10,role:[0,2,5,6,8,15],roll:6,room:[0,19],root:[0,5,9,13,18],rotat:[1,8,9,10],rotation_matrix:9,roughli:[1,3],round:[0,7,9,13],routin:[13,16],row:[0,1,2,5,6,7,9,11,16],rr:5,rrr:5,rthe:5,rug:13,rule:[0,1,5,6,13],run:[0,1,2,3,4,5,6,8,9,11,13,15],runtim:[1,6,14],runtimewarn:[1,6],rust:[0,15,16],rustad:19,rvert:1,rvert_2:1,s:[0,1,2,3,4,5,6,7,9,11,12,13,15,16,17,18,19],s_1:6,s_:[3,6],s_i:[6,7],s_j:6,s_k:6,saddl:13,safe:[],sai:[0,1,2,3,4,5,6,7,8,9,10,11,12,16,18],said:[6,9,13],sake:[0,5,7,11],sale:0,same:[0,1,2,3,4,5,6,8,9,11,12,13,14,16,18],samm:10,sampl:[0,1,2,3,4,5,6,7,8,9,10,13,14,15,16,18],sample_vari:14,sample_weight:[3,4],sampleexptvari:18,sastri:11,satisfactori:0,satisfi:[1,2,3,6,8,13,16,18],satur:[1,6],save:[0,4,6,7,9],save_fig:[0,6,7,9,10],savefig:[0,4,6,7,9,18],savetxt:4,saw:5,scalabl:10,scalar:[2,5,6,10,13],scale:[0,1,3,5,6,7,8,9,10,11,12,13,15,16,19],scale_mean:4,scale_std:4,scalei:[],scaler:[0,7,8,9,10,11],scalex:[],scan:[5,7],scari:5,scatter:[0,1,6,7,8,9,14],scenario:[6,13],schedul:13,scheme:[1,13],schrage:18,scienc:[0,1,10,12,13,15,17,18,20],scientif:[0,15],scientist:0,scikit:[3,5,6,7,8,9,10,13,15,16,17,20],scikitplot:[7,10],scipi:[0,3,5,6,13,15,16],scl:6,score:[0,1,3,6,7,9,10,11,19],scores_kfold:6,scratch:1,sdg:13,seaborn:[0,1,3,6,7],seamless:[0,15],search:[0,1,3,5,9,13],sec:6,second:[0,2,3,4,5,6,7,8,9,11,12,13,14,15,16,18],secondeigvector:11,secondli:12,section:[4,11,16,17,18],sector:0,see:[0,1,2,3,4,5,6,7,8,10,11,12,13,14,15,16,17,18],seed:[0,1,2,3,4,5,6,8,9,11,13,14,18],seed_imag:4,seek:[1,2,8],seem:[1,3,4],seemingli:0,seen:[0,1,3,5,10,12,18],segment:13,seismic:6,seldomli:0,select:[1,5,6,8,9,10,11,17,18,20],self:[1,3,4,5,20],sell:4,semest:[7,17],semi:[8,13],semilogx:6,send:[5,12,13,19],senior:17,sens:[0,4,6,8],sensibl:3,sensit:[0,5,6,9,13],sent:2,sentenc:[4,12],sep:[],separ:[0,1,2,4,6,8,9,12,14,15,18],sequenc:[2,3,4,7,9,10,12,13,15,16,18],sequenti:[1,3,4,10,12,18],seri:[0,1,2,3,4,5,6,10,11,12,13,16,17],serif:[0,7,18],serv:[0,1,2,3,5,7,13,20],session:[1,17],set:[1,4,5,6,7,8,10,11,13,14,15,16,18],set_major_formatt:6,set_major_loc:6,set_stream:5,set_tick:[1,8],set_ticklabel:1,set_titl:[0,1,2,3,7,12,14],set_xlabel:[0,1,2,3,7,12],set_xlim:[7,12],set_xticklabel:1,set_ylabel:[0,1,2,3,7],set_ylim:[7,12],set_ytick:7,set_yticklabel:[1,6],set_zlim:6,seth:4,setiosflag:[],setminu:6,setosa:[8,9],setosa_or_versicolor:8,setp:6,setprecis:[],setse:5,setup:[1,4,5,6,8,15],setw:[],sever:[0,3,5,6,7,8,9,11,12,13,15,16,17,18],sgd:[1,3],sgd_clf:8,sgdclassifi:8,sgdreg:13,sgdregressor:13,sgn:5,sh:[],shallow:13,shape:[0,1,2,3,4,5,6,7,8,9,10,11,13,14,16],share:[1,3],she:7,shift:[1,6,12,18],ship:3,shortcom:13,shorten:4,shorter:18,shortli:16,should:[0,2,3,5,6,8,9,11,12,13,16,18],should_sync:[3,4],show:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],show_shap:4,shown:[0,4,5,7,8,11,12,13,16],showpoint:[],shrink:[3,5,6,8,11],shrinkag:[5,6],shrunk:11,shuffl:[0,1,3,4,6,13],side:[0,2,5,8,12,13,16],sigh:15,sigma0:18,sigma1:18,sigma2:18,sigma:[0,1,5,6,7,10,11,12,13,16,18],sigma_0:5,sigma_1:5,sigma_2:5,sigma_:[5,16],sigma_fn:[7,12],sigma_i:[0,5],sigma_j:5,sigma_m:[6,18],sigma_n:[11,18],sigma_t:13,sigma_x:18,sigmoid:[1,2,4,7,8,10,12,13],sigmundson:[6,19],sign:[1,2,7,8,10,18],signal:[1,3,10,12],signatur:[3,4],signifi:4,signific:1,significantli:[1,13,18],sigurd:19,sim:[4,5,6,13,18],similar:[0,1,2,3,4,5,6,7,8,9,10,11,13,14,15,16],similarli:[0,1,3,5,8,10,18],simpl:[1,2,3,5,6,7,8,10,11,12,14,15,16,18],simple_rnn:4,simplepredict:10,simpler:[0,1,5,6,13,15],simplernn:4,simplest:[0,1,3,4,9,10,12,14],simpletre:10,simpli:[0,1,2,4,5,6,8,9,10,11,12,15,16,18],simplic:[2,5,6,7,8,9,10,11,12,14],simplicti:5,simplifi:[0,6,9,15],simplist:[3,6,18],simul:6,simultan:6,sin:[0,1,2,3,4,9,12,13,16],sinc:[0,1,2,3,5,6,7,8,9,10,11,13,16,18,20],sine:[3,12],singl:[0,1,2,3,5,6,7,8,9,12,13,16,18],singular:[0,6,13,16,17],sinusoid:3,site:[0,1,2,3,4,6,7,8,11,13,14,17],situat:[0,4,5,7,13,18],six:[3,18],size:[0,1,2,3,4,5,6,8,9,10,11,13,16,18],sketch:10,ski:9,skill:0,skip:[3,4,11],skiprow:[],skl:[0,6],sklearn:[0,1,3,5,6,7,8,9,10,11,13,14],skplt:[7,10],sl:6,slack:8,slice:[2,16],slide:[0,3,18],slight:[6,13],slightli:[1,2,3,5,6,7,10,18],slope:[8,11,12],slow:[0,2,8,13],slower:[5,16],slowest:16,slowli:12,slp:1,small:[0,1,2,3,5,6,8,9,10,11,12,13,15,16,18],smaller:[0,1,2,5,6,8,9,11,13,18],smallest:[0,4,14],smallest_row_index:14,smooth:[0,3,6,13],sn:[0,1,3,6,7],sne:11,so:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18],soar:6,social:0,soft:[1,7,10,12],soften:8,softmax:[3,7],softwar:[0,8,15,16,17],sol:8,sole:[0,6],solid:[0,7],solitem:5,soltyp:5,solut:[0,1,2,3,5,6,8,10,11,13,16,18],solutionsummari:5,soluton:2,solv:[0,1,3,5,6,8,10,11,12,13,16,17],solve_expdec:2,solve_ode_deep_neural_network:2,solve_ode_neural_network:2,solve_pde_deep_neural_network:2,solveod:2,solveode_popul:2,solver:[2,5,7,8,9,10,11,16],some:[0,1,2,3,4,5,6,7,8,9,10,11,12,14,17,18],some_model:6,somehow:4,someth:[0,1,3,4,7,9,11,18],sometim:[0,1,11,12,13,14],soon:16,sophist:0,sopt:13,sort:[5,6,9,11,18],sound:[3,5],sourc:[0,1,3,6,15,16,18],space:[0,1,4,5,8,9,11,12,13,14,18],span:[0,3,5,9,11,16],spare:1,spars:[3,5,6,16],sparse_mtx:16,sparsecategoricalcrossentropi:3,sparsiti:10,spatial:[1,2,3,12],spdiag:5,speak:18,special:[6,7,10,12,13,16,18],specif:[0,1,2,3,4,5,6,7,8,9,11,12,15,16,18],specifi:[0,3,5,6,7,9,11,13,14,18],specifici:[0,10],spectacular:3,spectral:1,speech:[0,1,3,4,12],speed:[1,2,4,13],spend:18,sphere:0,spin:6,spite:0,spline:8,split:[1,3,4,5,6,8,9,10,11,14,18],splite:0,splitter:[1,10],spmatrix:5,spontan:18,spot:3,spread:[0,11,18],springer:20,spuriou:13,sqquar:5,sqrsignal:3,sqrt:[0,3,4,5,6,8,10,11,13,18],squar:[1,2,3,4,7,8,9,11,13,14,15,16,17,18],squarederror:10,squaredeuclidean:14,squash:12,squeez:2,srand:[],srtm:6,srtm_data_norway_1:6,sse4:[],stabil:5,stabl:[0,4,5,6,7,9,11,15],stack:[2,3,4],stacklevel:0,stage:[5,13],stai:[0,2,4,5,11],stand:[0,5,9,12],standard:[0,1,4,5,6,7,8,10,12,16,18],standard_basi:2,standardscal:[0,6,7,8,9,10,11],stanford:13,start:[0,1,2,3,4,5,6,8,9,10,11,12,13,14,16,17,18],start_box:[2,13],start_nod:[2,13],start_tim:14,startpoint:[],stat:6,state:[1,2,4,5,6,7,8,10,11,12,13,15,18],statement:[0,7,16],statist:[0,1,3,4,7,9,10,11,12,13,14,16,17,20],statu:[0,7,11],stavang:6,std:[0,4,6],stdev:[],stdout:5,steep:13,step:[0,1,2,3,4,6,7,9,10,11,12,13,14,16],step_fn:[7,12],step_length:13,steps_list:9,steps_per_epoch:[3,4],stereo:3,stian:19,still:[2,3,5,6,11,13,18],stimuli:12,stk2100:20,stk3155:17,stk4021:20,stk4051:20,stk4155:17,stk5000:20,stk:20,stochast:[0,1,5,6,8,11,12,17],stock:4,stoke:12,stone:[0,7],stop:[1,4,7,9,11,13,14],storag:5,store:[0,1,2,3,6,11,13,18],storehaug:19,str:[1,3,4],straight:[0,6,8,13],straightforward:[0,2,3,5,6,8,9,10,13,16],strategi:[0,1,9],stratifi:6,streamtyp:5,strength:[0,5,14],stretch:11,strict:[8,13],strictli:[8,13],stride:[4,16],strike:6,string:1,stroke:7,strong:[3,6,9,10,12,16,18],strongli:[0,8,15,16],stronli:0,structur:[0,1,2,3,6,9,10,12,15],stuck:[1,13],student:[0,17,19,20],studi:[0,3,4,5,6,7,8,11,12,13,15,20],studier:[17,20],style:[0,7,9,16],sub:[9,12],subarg:[2,13],subdivid:[0,16],subfield:0,subject:[5,6,8,18],subplot:[0,1,3,4,6,7,8,9,10,13,14],subplots_adjust:[8,18],subprogram:16,subract:0,subroutin:0,subscript:1,subsequ:[1,4,5,6,12,16,18],subset:[1,6,9,12,13,15],subspac:[0,8,11],substanti:[9,10],substep:11,substitut:[3,6,12,16],subsubset:9,subtask:6,subtl:1,subtract:[0,4,5,6,11,13,16,18],subtre:9,subval:[2,13],succeed:[0,4],success:[3,7,9,13,18],successfulli:[4,9],sucess:[],sudo:[0,15],suffer:[0,1,2,5,10],suffici:[1,6,8,11,13],suggest:[1,13,20],suit:[8,12],suitabl:[0,18],sum:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],sum_:[0,1,2,3,5,6,7,8,9,10,11,12,13,14,16,18],sum_i:[2,5,6,8,13],sum_j:6,sum_k:[6,8,12,16],sum_m:3,sum_n:3,sum_nx_:3,summar:[5,6,9],summari:[1,3,4,10,17],summat:3,sunni:9,superfici:3,superscript:[1,12],supervis:[0,5,6,7,9,12,15],supplement:7,support:[0,1,9,10,11,13,15,17],suppos:[0,5,6,7,8,10,11,12,13,16],suppress:[5,13],sure:[1,4,6],surf:6,surfac:[0,6],surpass:6,surpris:0,surround:[3,15],survei:[0,5,6],svc:[8,9,10],svd:[0,6,11,17],svdinv:5,svm:[8,9,10,11],svm_clf:[8,10],swap:2,swapax:2,swath:5,sy:[5,13],symbol:[1,5,11,13,15,18],symmeteri:1,symmetr:[0,5,8,11,12,13,16],symmetri:6,sympi:[0,15],synonim:18,syntax:[1,13],syntaxerror:1,syrk:5,system:[0,1,3,4,5,6,7,9,10,12,13,15,16,20],systemat:[4,6],t0:[3,6,13],t1:[2,13],t2:2,t3:2,t:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19],t_0:[2,9,13],t_1:13,t_:2,t_b:10,t_i:[1,2,12],t_j:[5,12],t_k:9,tabl:[9,18,19],tabul:0,tackl:4,tag:[2,3,4,5,6,7,12,13,14,16,18],taht:0,tail:18,tailor:[2,8,11],taiwan:0,take:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,18],taken:[0,1,3,6,10,13,16],tan:3,tangent:[1,4,12,13],tanh:[1,4,7,8,12,13],tape:[3,4],target:[0,1,3,4,5,6,7,8,9,10,11,12,13],target_nam:9,task:[0,1,3,5,6,9,11,12,14],tau:[3,5,18],tax:0,taylor:[2,13],taylornr:13,tc:8,team:1,teaser:0,technic:[0,5,6,13],techniqu:[0,1,8,10,13,15,17,18,20],technolog:[0,1],tek5040:20,tell:[0,4,6,10,11,13,18],temp1:1,temp2:1,temp:1,temperatur:[0,9],temporarili:1,ten:3,tend:[3,5,6,8,9,10,12,13,14],tendenc:0,tension:6,tensor:[3,4],tensorflow:[0,2,4,8,14,15,16,17,20],term1:[5,6,11],term2:[5,6,11],term3:[5,6,11],term4:[5,6,11],term:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,18],termin:[0,4,5,9,10,13],terrain1:6,terrain:6,test:[3,4,5,6,7,8,9,10,13,14,16,18],test_acc:3,test_accuraci:[1,3],test_error:6,test_imag:[3,4],test_ind:6,test_input:4,test_label:[3,4],test_loss:3,test_pr:1,test_predict:1,test_rnn:4,test_scor:[7,10],test_siz:[0,1,3,5,6,10],test_split:9,testerror:[0,6],testi:4,testpredict:4,testx:4,text:[0,1,2,4,5,8,9,11,13,16,17,18,20],textual:9,textur:1,tf:[1,3,4,13,14],tfe_py_execut:[3,4],th:[0,1,2,5,6,7,9,12,13,14,16,18],than:[0,1,2,3,4,5,6,7,9,10,11,12,13,15,18],thank:[4,6],theano:[1,15],thei:[0,1,2,3,4,5,6,7,8,9,11,12,13,16,18],them:[0,1,3,4,6,8,9,10,11,12,13,16],theme:0,themselv:[0,18],thenc:6,theorem:[2,6,7],theoret:[0,4,10],theori:[0,1,3,8,9,12,13,15,17,20],thereaft:[0,5,6,11,12,16],therebi:[0,5,7,11],therefor:[0,1,2,3,4,6,7,8,11,13,18],therein:11,thereof:[0,6,13],theta:[1,4,13,18],theta_:[1,13],theta_i:1,theta_k:[],theta_linreg:13,theta_t:13,thi:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20],thing:[0,1,2,4,5,7,9,18],think:[0,1,3,4,6,9,12,13,14,18],third:[0,3,6,13],thirti:7,those:[3,5,6,8,9,10,11,16,17],though:[1,2,3,4,13,16,18],thought:[6,14,18],thousand:[0,1],thread:[3,4],three:[0,1,3,5,6,8,9,12,16,17,18,19],threshold:[1,3,9,10,11,12,13],through:[0,1,2,3,4,5,6,8,11,12,13,14,15,16,18],throughout:[0,4,5,14,15,16,18],thu:[0,1,2,5,6,7,8,10,11,12,13,19],thumb:[0,6],thursdai:17,tibshirani:[6,17,20],tick_param:6,ticker:[6,13,18],tif:6,tight_layout:[1,7],tightli:11,tild:[0,5,6,11,18],till:[0,4,7,8,9,10,12,16],time:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,17,18],timefunct:[],timeit:4,timer:4,tini:1,tip:3,titl:[0,1,2,3,4,6,7,8,9,10,13,18],tmp:13,tmp_log:[3,4],tn:[2,3],to_categor:[1,3,4],to_categorical_numpi:1,to_numer:[0,6],to_str:[],todai:3,togeth:[0,3,6,8,11],toi:[13,14],told:13,toler:[2,6,14],tolist:4,tomographi:12,too:[0,2,4,5,6,9,11,13,18,20],took:8,tool:[0,1,3,6,13,15],toolbox:8,top:[0,3,5,6,9,10,15],top_node_typ:2,top_trac:2,topic:[0,5,6,7,8,15,17,20],topolog:[1,3,12],toposort:2,torkjellsdatt:19,toss:[10,18],total:[0,1,2,3,4,6,7,8,10,11,12,13,14,16,18,19],total_loss:4,totalclustervari:14,totalscatt:14,totalvari:[],toward:[1,2,7,12,13],town:0,tp:4,tpng:9,tqdm:6,tr:0,trace:[2,13],trace_stack:[2,13],traceback:[0,2,3,4,5,6,7,8,9,10,13],traceback_util:[3,4],tracer:[2,13],track:[3,13,14,16],tract:0,tractabl:0,trade:[5,9],tradeoff:[0,5,17],tradit:[0,1,4,6],train:[2,3,5,6,8,9,10,11,12,13],train_accuraci:[0,1,3],train_dataset:4,train_end:[0,1],train_error:6,train_funct:[3,4],train_imag:[3,4],train_ind:6,train_label:[3,4],train_pr:1,train_siz:[0,1,3],train_step:4,train_test_split:[0,1,3,5,6,7,9,10,11],train_test_split_numpi:[0,1],trainabl:4,trainable_vari:4,trained_model:6,trainerror:0,traini:4,training_checkpoint:4,training_dataset:4,training_gradi:13,training_gradient_fun:13,training_loss:13,trainingerror:6,trainpredict:4,trainscor:4,trainx:4,trait:0,trajectori:4,tran:5,transfer:9,transform:[0,5,6,7,8,9,10,11,12,13,15,16],transit:[6,12],translat:[1,4,6,10],transpos:[1,5,11,16],travers:[0,5],treat:[0,1,3,6,12,13,18],tree:[0,1,6,15,17],tree_clf:[9,10],tree_clf_:9,tree_clf_sr:9,tree_reg1:9,tree_reg2:9,tree_reg:9,trend:18,trevor:20,tri:[2,3,4,9,13],triain:0,trial:[0,2,4,6,13,18],triangl:13,triangular:16,trick:[3,4,8,11,13,18],trickier:18,tridiagon:16,trillion:15,trivial:[0,1,5,11,18],troubl:[0,8,12],truck:3,true_beta:6,true_divid:1,true_fun:6,tucker:8,tumor:[7,9],tumour:7,tunabl:1,tune:[4,9,13,16],tup:[],tupl:[2,13],turn:[0,1,5,6,7,8,9,10,11,12,13,16,18],tutori:[1,4],tv:2,tveito:2,tweak:[1,4,10,18],twice:13,twist:11,twister:[],two:[0,1,2,4,5,6,7,9,10,11,12,13,16,17,18,20],tx:13,tx_1:13,txt:4,ty:13,type:[0,1,3,6,8,10,13,16,18],typeerror:[2,13],typic:[0,1,2,3,4,5,7,9,10,12,13,18],u:[0,2,5,6,10,11,12,16],u_:16,u_i:12,u_m:10,ua:0,ubuntu:[0,15],uci:0,uio:[17,19,20],un:14,unari:16,unary_f:[2,13],unary_oper:[2,13],unary_to_nari:2,unbalanc:[6,9],unbias:[0,5,6],uncent:6,uncertainti:[0,5],uncertitud:18,unchang:[1,3],uncorrel:[10,18],undefin:5,under:[0,1,5,6,10,13,15],underdetermin:0,underfit:[1,6],underflowproblem:5,undergo:5,undergradu:17,underli:[0,1,9,13,18],underset:[4,14],understand:[0,1,3,5,6,10,13,14,15],understood:[8,13],undesir:8,undetermin:[5,8],undo:4,unexpect:6,unexpected:18,unfair:6,unfortun:[1,8,9,10],unicode_liter:[8,9],uniform:[0,1,5,6,11,13,18],uniform_real_distribut:[],uniformli:[13,18],unifrompdf:18,unimport:13,union:[5,6],uniqu:[0,2,6,13,14,16],unique_cluster_label:14,unit:[0,1,3,4,5,10,12,18],unitari:[5,6,16],unitarili:16,uniti:18,univari:18,univers:[0,1,2,13,15,17,19],unix:1,unknow:[0,16],unknown:[0,1,3,4,5,6,8,10,16],unknowwn:12,unlabel:1,unless:[0,3,6,11,13],unlik:[1,3,8,13],unnecessarili:9,unord:3,unravel:1,unrol:[3,11],unseen:[0,7,9],unstabl:1,unsupervis:[0,1,4,12,15,17],unsymmetr:16,until:[1,2,4,9,12,13,14],untouch:0,unusu:12,up:[1,3,4,5,6,8,10,11,13,14,15,16,17,18],updat:[1,2,10,12,13,14],upload:[15,20],upon:[1,6,11,16],upper:[0,8,9,16],uppercas:16,upsampl:4,upscal:4,us:[4,5,6,8,9,10,11,12,14,16,17,18,20],usag:[0,8,15],usd10000:0,usd:0,use_bia:4,use_multiprocess:[3,4],usecol:0,useless:1,user:[0,1,2,3,4,6,7,8,11,14,15,16],userwarn:[3,4,6,14],usetex:18,usg:6,usr:18,usual:[0,3,4,7,12,13,14],ut:5,util:[0,1,3,4,6,7,10,14],ux:16,v0:18,v1:18,v2:18,v:[2,4,5,6,11,13,15],v_0:11,va:1,val:13,val_accuraci:3,val_loss:4,vale:2,valid:[0,1,4,7,9,10,13,15,17,18],validation_batch_s:[3,4],validation_data:[3,4],validation_freq:[3,4],validation_split:[3,4],validation_step:[3,4],valu:[0,1,2,3,4,6,7,8,9,10,12,13,14,15,16,17],valuat:9,valueerror:[0,2],valy:4,van:0,vandenbergh:[8,13],vandermond:0,vanilla:[0,6,11,14],vanish:[1,4,13,18],var_x:18,varabl:8,varepsilon:[5,6],varepsilon_:[5,6],varepsilon_i:[5,6],vari:[0,1,3,5,6,10],variabl:[0,1,2,3,4,5,6,7,8,10,11,12,13,14,16],varianc:[0,1,5,7,9,10,11,13,14,15,16,17,18],variance_i:[5,11],variance_x:[5,11],variant:[0,1,6,8,12,13],variat:[3,4,11],varieti:[0,3,12,15],variou:[1,3,5,6,7,8,9,11,12,13,15,16,18],vartempvec:[],varvec:[],varydimens:4,vastli:3,vaue:1,vault:0,vdot:[2,13],vec:6,vector:[0,1,2,3,4,5,6,7,9,10,11,13,14,15,17],vector_mean:14,ventur:[0,8,15],verbos:[1,3,4],veri:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,18,20],verifi:[3,11,16],versatil:8,versicolor:[8,9],version:[0,3,4,10,13,14,15,16,18],versu:1,vert:[0,1,5,6,7,8,9,11,13],vert_1:[5,6],vert_2:[5,6,11],via:[0,5,6,7,8,9,10,11,12,15,16,17,18],vidal:11,video:[0,1,12,15,17],view:[1,3,5,6,12,13,17,18,20],violat:8,virginica:9,viridi:[0,1,2,3],virtual:1,viscos:13,viscou:13,visibledeprecationwarn:2,vision:[0,3],visual:[0,3,11,12,15],visualis:1,viz:[6,8,18],vjp:[2,13],vjp_0:[],vjp_0_fun:[],vjp_1:[],vjp_1_fun:[],vjp_argnum:[],vjpfun:2,vjpmaker:[],vjpnode:[2,13],vmax:[1,6],vmc:[],vmin:[1,6],voic:3,volum:[0,3],vote:10,voting_clf:10,votingclassifi:10,votingsimpl:10,vrtx:17,vs:[0,4,6],vspace:[2,13],vstack:[5,11,16,18],vt:5,w1:8,w2:[8,11],w3:8,w:[0,1,2,3,4,5,6,7,8,10,11,12,13,14,16,18],w_1:[8,16],w_1x_1:8,w_1x_:8,w_2:[8,16],w_2x_2:8,w_2x_:8,w_3:16,w_4:16,w_:[1,12],w_hidden:2,w_i:[1,2,10],w_ix_i:12,w_j:16,w_m:16,w_output:2,w_px_:8,w_px_p:8,wa:[0,1,3,4,5,6,7,10,11,12,13,14,16],wai:[0,1,2,3,4,5,6,7,8,10,11,12,13,14,16,18],walk:9,walker:18,wang:0,want:[0,1,2,3,4,5,6,8,9,10,11,12,13,14,15,18],warn:[1,3,4,8,14],warrant:6,wast:3,watch:[3,4,15],wave:3,wavelet:8,we:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20],weak:[9,10,14],weather:[1,12],web:[15,17],webpag:17,websit:[6,16,17],wedg:[8,18],wednesdai:17,wee:11,week:[5,6,7],weekli:[15,20],weight:[0,1,2,3,6,7,9,10,12,13,18],weigth:2,welcom:[8,15],well:[0,1,2,3,4,5,6,7,8,9,10,12,13,15,16,17,18,20],went:8,were:[0,1,3,4,5,6,7,8,10,11,12,14,18],wessel:0,westbi:19,westby:19,what:[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18],whatev:3,when:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],whenev:[13,18],where:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19],wherea:[6,18],wherein:[1,12],whether:[0,3,5,7,9,18],which:[0,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19],whichev:[1,3],white:9,who:[0,17],whole:[1,3,4,5,9,11,13],whose:[0,6,10,18],whow:[5,11],why:[0,1,3,6,13],wide:[0,1,3,6,7,12,15,16],widehat:6,width:[0,3,8,9],wieringen:0,win:10,wind:9,wing:19,winther:2,wiothout:6,wiscons:7,wisconsin:10,wisdom:6,wise:[0,1,5,12,13],wish:[0,2,5,7,8,11,13,14,16],with_std:0,wither:6,within:[0,2,3,4,7,9,12,13,14,18,20],withinclust:14,without:[0,1,5,6,8,9,11,12,13],won:0,wonder:8,word:[0,1,3,4,5,6,14,18],work:[0,1,4,6,7,8,9,13,15,17,18],worker:[3,4],workshop:17,world:[0,8],worldwid:0,wors:[0,1,3,4,6],worst:[],worth:9,would:[0,1,3,5,6,7,8,9,10,11,12,13,16,18],wrap:[2,6,16,17],wrap_util:[2,13],wrapper:0,write:[0,1,2,3,5,6,7,8,12,13,16],written:[0,2,3,5,11,12,13,15,16,18],wrong:[1,8],wrongli:10,wrote:[5,11],wrt:[2,10,13],wth:10,www:[15,16,17,20],wx_1:8,x0:8,x1:[4,8,9,10,13],x1_exampl:8,x1d:8,x2:[8,9,10,13],x2d:[8,11],x2d_train:11,x2dsl:11,x3:8,x:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],x_0:[0,5,11,16],x_1:[0,2,5,6,7,8,9,10,11,13,16,18],x_2:[0,2,5,6,7,8,9,10,11,13,16,18],x_3:[8,16,18],x_4:16,x_:[0,2,3,5,6,8,10,11,13,14,16,18],x_center:11,x_data:1,x_data_ful:1,x_hidden:2,x_i:[0,1,2,5,6,7,8,9,10,11,12,13,14,16,18],x_input:2,x_ix_:0,x_iy_i:8,x_j:[0,2,8,9,12,18],x_jy_j:8,x_k:[12,14,16,18],x_l:18,x_m:[6,12,16,18],x_n:[0,2,3,6,8,11,12,13,16,18],x_new:[9,10],x_offset:6,x_output:2,x_p:[3,7,9],x_poli:9,x_poly10:9,x_pred:4,x_prev:2,x_reduc:11,x_scale:8,x_test:[0,1,3,5,6,7,9,10,11],x_test_own:6,x_test_scal:[0,6,7,9,10,11],x_tot:4,x_train:[0,1,3,4,5,6,7,9,10,11],x_train_mean:6,x_train_own:6,x_train_scal:[0,6,7,9,10,11],x_val:1,xarrai:15,xavier:1,xbnew:13,xcode:[0,15],xdclassiffierconfus:10,xdclassiffierroc:10,xg_clf:10,xgb:10,xgbclassifi:10,xgboost:9,xgboot:10,xgbregressor:10,xgparam:10,xgtree:10,xi:[8,13],xi_1:8,xi_:8,xi_i:8,xk:8,xlabel:[0,1,2,3,4,5,6,7,8,9,10,13,18],xlim:[6,10],xm:9,xmesh:13,xnew:[0,13],xp:18,xpanda:0,xpd:[5,11],xplot:0,xs:9,xscale:0,xsr:9,xt_x:13,xtest:6,xtick:[3,6,8,9],xtrain:6,xu:0,xx:[0,5,16],xy:[0,6,8,16],xytext:8,xz:16,y1:4,y2:4,y3:4,y:[0,1,3,4,5,6,7,8,9,10,11,12,13,14,16,18],y_0:[0,5,11,16],y_1:[0,5,8,9,11,13,16],y_1y_1:8,y_1y_1k:8,y_1y_2:8,y_1y_2k:8,y_1y_n:8,y_1y_nk:8,y_2:[0,5,8,9,11,16],y_2y_1:8,y_2y_1k:8,y_2y_2:8,y_2y_2k:8,y_3:[0,9,16],y_4:16,y_:[0,1,5,6,10,11,16],y_data:[0,1,5,6],y_data_ful:1,y_decis:8,y_fit:0,y_i:[0,1,5,6,7,8,9,10,11,12,13,16],y_if_:10,y_ix_:0,y_ix_i:[7,8,13],y_iy_jk:8,y_j:[6,8,12],y_k:12,y_m:16,y_model:[0,4,5,6],y_n:[8,13],y_ny_1:8,y_ny_1k:8,y_ny_2:8,y_ny_2k:8,y_ny_n:8,y_ny_nk:8,y_offset:6,y_plot:9,y_pred1:9,y_pred2:9,y_pred:[0,1,4,6,7,8,9,10],y_pred_rf:10,y_pred_tre:10,y_proba:[7,10],y_scaler:6,y_test:[0,1,3,4,5,6,7,9,10,11],y_test_onehot:1,y_test_predict:0,y_tot:4,y_train:[0,1,3,4,5,6,7,9,10,11],y_train_mean:6,y_train_onehot:1,y_train_predict:0,y_train_scal:6,y_val:1,ye:[3,6,7],year:[0,15],yet:[0,1,6,8,11,13],yhd5w:17,yi:13,yield:[0,2,5,6,8,10,12,13,14,16,18],yk:8,ylabel:[0,1,2,3,4,5,6,7,8,9,10,13,18],ylim:[3,6],ym:9,ymesh:13,yn:0,yo:[8,9,10],yoshua:[1,20],you:[0,1,2,3,4,5,6,8,9,10,11,13,15,16,18,20],young:0,your:[1,2,4,5,6,8,11,13,15,16],yourself:[11,13],youtu:17,youtub:15,ypred:6,ypredict2:13,ypredict:[0,13],ypredictlasso:5,ypredictol:5,ypredictown:6,ypredictownridg:6,ypredictridg:[5,6],ypredictskl:6,ys:9,ytest:6,ytick:[3,6,8,9],ytild:[0,6],ytildelasso:5,ytildenp:0,ytildeol:5,ytildeownridg:6,ytilderidg:[5,6],ytrain:6,yx:16,yy:16,yz:16,z:[0,1,2,3,4,5,6,7,8,9,11,12,13,16,18],z_0:16,z_1:16,z_2:16,z_:[1,2,12,16],z_c:1,z_h:1,z_hidden:2,z_i:[1,12],z_j:[1,12],z_k:12,z_m:1,z_mod:9,z_o:1,z_output:2,zaman:18,zaxi:6,zero:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18],zeros_lik:4,zfill:4,zip:[2,4,6,13],zl:5,zm_h:0,zn:0,zone:0,zx:16,zy:16,zz:16},titles:["<span class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks"],titleterms:{"1":[],"10":17,"11":17,"12":17,"13":[],"14":17,"15":[],"16":17,"17":17,"18":17,"19":17,"2":[0,17],"20":[],"2021":[],"2022":19,"21":17,"22":17,"23":17,"24":17,"25":17,"26":17,"27":[],"28":17,"29":17,"3":17,"30":17,"31":17,"34":17,"35":17,"36":17,"37":17,"38":17,"39":17,"4":17,"40":17,"41":17,"4155":[],"42":17,"43":17,"44":17,"45":17,"46":17,"47":17,"5":17,"6":[],"7":17,"8":[],"9":17,"case":[8,10,18],"do":1,"final":12,"function":[0,1,6,7,8,10,11,12,13,18],"import":[5,16],"new":4,A:[0,1,4,8,9],And:13,Ising:6,The:[0,1,2,3,5,6,7,8,9,11,12,15],With:4,activ:[1,12],actual:[],ad:6,adaboost:10,adam:13,adapt:10,adjust:1,adversari:4,again:[3,9],aim:[8,9],algebra:16,algorithm:[9,10,11,12],algortithm:13,all:8,an:[0,4,10],analys:5,analysi:[0,5,6,11,15,18],ani:13,anoth:9,appli:15,approach:[0,8,14],approxim:12,architectur:1,arrai:16,assist:19,august:17,autocorrel:18,autograd:[2,13],automat:13,back:[1,11,12],background:15,bag:10,base:13,basic:[0,5,7,9,10,11,16],batch:1,bay:5,befor:11,better:8,bia:6,binari:1,binomi:[],bird:10,block:[],boost:10,bootstrap:[6,10],boston:0,breast:1,bring:12,build:[1,3,9],calcul:[],cancer:[1,7,9,11],cart:9,central:[13,15,18],chain:12,chang:10,chi:0,choos:1,cifar01:3,classic:11,classif:[1,9,10],classifi:8,clip:1,cluster:14,cnn:3,code:[0,1,2,5,9,11,12,14],collect:[1,3],compar:[2,10],complex:[0,6],complic:6,compon:11,comput:9,con:9,concept:18,conjug:13,continu:[],convex:[8,13],convolut:[3,12],correl:11,cost:[1,10],cours:[15,20],covari:[5,11,18],cross:6,cumul:[],data:[0,1,3,6,7,9,11,15,18],dataset:[1,3],decai:2,decis:[9,10],decomposit:[5,11,16],deep:[1,2],defin:1,definit:[],degre:0,demonstr:[],dens:0,deriv:[5,12],descent:[2,10,13],detail:3,develop:1,deviat:[],diagon:11,dice:[],differ:8,differenti:[2,13],diffus:2,dimension:[2,3,8],disadvantag:9,discret:18,disguis:[],distribut:[5,18],domain:18,down:1,dropout:1,element:[0,18],elimin:16,ensembl:10,entropi:9,environ:0,equat:[0,2,12],error:[0,10],euler:2,evalu:1,event:[],exampl:[0,1,2,3,4,6,7,8,9,10],exercis:[0,6],expect:18,experi:18,explor:0,exponenti:2,extrapol:4,extrem:10,ey:10,fall:19,famili:1,famou:16,featur:[9,16],feed:[1,12],fine:1,first:[4,12],fit:[0,10],forc:3,forest:10,forward:[1,2,12],fourier:3,frank:6,freedom:0,frequentist:0,from:[5,10,12],full:2,further:[3,5],fy:[],gan:4,gaussian:16,gd:13,gener:[4,9],geometr:11,gini:9,good:0,grade:19,gradient:[1,2,10,13],growth:2,ha:15,handl:16,hidden:2,hous:0,how:[],hyperparamet:1,hyperplan:8,i:1,id3:9,idea:11,implement:1,implic:5,improv:1,includ:13,increment:11,index:9,inform:19,input:2,instal:15,instructor:19,interpret:[5,11],introduc:11,introduct:[0,6,15,16],invers:[5,16],iter:10,its:[],jackknif:[],jungl:10,kera:[1,3],kernel:[8,11],lagrangian:8,lasso:[5,6],later:5,layer:[1,2,3,12],learn:[0,1,2,11,13,14,15],least:[5,6],level:10,librari:15,likelihood:7,limit:[1,13,18],linear:[0,8,13,16],link:[5,11,17,20],logist:[7,13],lu:16,machin:[0,8,13,15],main:18,make:[0,9,10],mani:[10,12],materi:17,math:5,mathemat:[3,5,8],matric:[5,16],matrix:[1,5,11,12,16],matter:0,mean:0,meet:[5,10,18],mercer:8,mersenn:[],method:[6,9,10,13],mlp:12,mnist:[3,4],model:[0,1,4,6,12],moment:[],momentum:13,moon:[8,9],more:[3,6,16],multilay:12,multipl:[1,3],multipli:8,name:[],network:[1,2,3,4,7,12],neural:[1,2,3,4,12],non:8,normal:[0,1],norwai:[],notat:12,novemb:17,now:[1,9],nuclear:0,nueral:7,number:[0,2,18],numer:[2,18],numpi:16,object:3,observ:[],obtain:11,octob:17,od:2,off:6,ol:[5,6,13],one:[2,12],oper:16,optim:[1,8,13,15],ordinari:[5,6],organ:0,oslo:20,other:[4,9,11,12,16],our:[0,4,5,11,13],outcom:15,output:2,overarch:[0,4,8,9],overview:10,own:[0,10,11],packag:16,part:[13,15],partial:2,pass:1,pca:11,pdf:18,perceptron:12,perform:[1,9],period:3,perspect:1,point:4,poisson:2,polynomi:3,popul:2,practic:13,pre:[1,3],predict:4,prerequisit:[3,15],princip:11,principl:3,pro:9,probabl:[5,18],problem:[1,2,13],procedur:9,process:[1,3],program:[2,13],project:6,prop:13,propag:[1,12],properti:[5,18],pseudo:[],python:[0,9,15,16],quick:8,ran0:[],random:[10,11,18],read:9,real:6,recip:[],recurr:[4,12],reduc:0,reduct:3,reformul:2,regress:[0,5,6,7,9,10,13],regular:1,relev:20,relu:1,remark:3,remind:[6,8],requir:[2,15],resampl:6,rescal:6,resourc:2,revisit:13,ridg:[5,6],rm:13,rng:[],rule:12,s:[8,10],sampl:11,schedul:17,schemat:9,scheme:2,scikit:[0,1,11],select:[],semest:19,septemb:17,set:[0,2,3,9,12],sgd:13,should:1,simpl:[0,4,9,13],singl:10,singular:[5,11],situat:[],soft:8,softmax:1,solv:2,solver:13,some:[13,16],specifi:2,split:0,squar:[0,5,6,10],standard:13,state:0,statist:[5,6,15,18],steepest:[10,13],stk3155:[],stochast:[13,18],superposit:3,supervis:1,support:8,svd:5,systemat:3,teach:[17,19],teacher:19,techniqu:[6,11],technolog:15,tensorflow:[1,3],test:[0,1],textbook:20,theorem:[5,8,11,12,18],theori:18,three:[],tip:13,togeth:12,top:1,toss:[],toward:11,trade:6,tradeoff:6,train:[0,1,4],transform:3,tree:[9,10],tune:1,two:[3,8,15],type:[2,4,12],uncorrel:[],uniform:[],univers:[12,20],unsupervis:14,up:[0,2,9,12],us:[0,1,2,3,7,13,15],valid:6,valu:[5,11,18],variabl:18,varianc:6,variou:0,vector:[8,12,16],view:[0,4,10],visual:[1,9],vs:3,wai:9,wave:2,week:17,weekli:17,what:0,which:1,why:[],wisconsin:7,write:[4,11],xgboost:10,your:[0,10]}})
\ No newline at end of file
diff --git a/doc/LectureNotes/schedule.md b/doc/LectureNotes/schedule.md
index 128ec011b..11030f65c 100644
--- a/doc/LectureNotes/schedule.md
+++ b/doc/LectureNotes/schedule.md
@@ -27,7 +27,7 @@ For the reading assignments we use the following abbreviations:
 - Lecture Thursday: Introduction to the course, what is Machine Learning and introduction to Linear Regression.
 - Video of Lecture August 25, 2022 at https://youtu.be/KL0m3-yhd5w
 - Lecture Friday: Basics of Linear Regression
-- Video of Lecture August 26, 2022 at 
+- Video of Lecture August 26, 2022 at https://youtu.be/ne_xCL2ctM0
 
 - Reading recommendations:
   - Refresh linear algebra, GBC chapters 1 and 2.
diff --git a/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016OLS.png b/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016OLS.png
index a96a76c74753cb5bc4229aaa9cc142e3c92a92e3..043b9723f5c17dca7964fbc42a5d8a8ad2541f01 100644
GIT binary patch
literal 12483
zcmd73WmsEV&_5ci6!#)U+MvZD6sPpyMO!FVC{QFop*RGW0!3R6PSE1Tp*TT8(U#!u
z?hsrG1n%bC|9#&t_rv{opJ(Su_TFpFS~F{AezRsxB3^4MlMpfxf<PdWS1JnHAP`n1
z@Okp^0q|s;i^CT9l5kTraMN*wy1_oUSb;P?xH;K7y4l;BKk=|~akX`Hfbfg(LwKLq
zxVbsGN(u;k`kw~;jxN>$s7!@HfCzz;ilHkAL_~G}!2+eG(}6&Og0B?*dFz?JKkt=p
zyzaMoJy>zRquXNpiQv`4prOx5$_LRT-#$g}Pj#Mu)sQ~wgp+T$e_A9TWOMzE;)(C9
zHnv`gux@4(C7|yieRRYSj!zf-mC4DzQqX+#0rtMiP79*xym%3_n(!{CtTrEAJbKk?
zTH&o*RD31fHab3*yeTp0^{r6~3<iIoDuQ7F-#XbKDvH?G+JO<_DqPQy3JPoq)Mj8X
zr7EEy1qA^+UP^d)NF27hf`Z&DEDkU@JMaI0%_GMVfe<Sw4Ae$Kcc_oPmDN1EMWY=|
zo7}dE-*SM3eWcIEbTH@zodd}M1ukq9YJxmua{MKQXD_OC7cG#nXPw0V{?A5#nsu=P
zMXX^v>U<3G1wxbbv{M&Jh0b)p5I$sG@^deLrJ%6G#fx<=eW(Q46|Ux3%0RA2PO^c)
z?$nV_6<~`{XaJ!v86AEdDDn3Jq<S`J7AuL@P&l4FGQ5|VNR5mb);2qDx`}qg{t}qM
z={fiw-P%4cc6u7JLYEgFuKtG0+YAI1ot%$JnWp@_YC1WF;Gm!=${NC@JLH4xVyhy1
z=KLB4&cef0m_j*_bC|DZ<xltKGE~PSl%kD`<ajBU8*w_^L^-zysX+#a9~tV|>SNI?
z2Roa^>`ZR;Y!QmlDxEvc_+W5P2nW*g<6H&{dMkHlz(U~M)rL(=*)^Rt7jI?wVZQ3w
z^8+y)Shzy;$MMF1zJH2IKNZm>3)tuK(QJ<VE!(3<9e2~ryNPh_)L4UEvLUbv7|E!4
z#}^X+r&+yoVtPxheE4*I$zHd}UV&zRkslkig>?OiHX2>ebc>0tqu5s89Cr~CZRB~n
ziwBH|jzn;k6^XkV#YeCA<*iWYp+0?hqO(>UCCQak!fT^dRU#lSNnvGT6sg{dnyX{O
z4|7F*(x^Hcf76so)tN)_r8{a*T)I0m$a&&y;DfU=Rmr28k@2|n0n@~A#vW9r%LU0<
z`KtO4d{MOqDX3Dp!A5?v@?SLBg^=$PWofcL!b-NpoFe;Dd)op*?E;xcx&QLS|DZF8
z)Zx=B+W4yaER;ThM`+~@S0WW1&^g<nEbdt{eZ|}GPH=Prktc5P8x`g6l)*ShSlrH@
zB3|LD(w^mUHA~Kne+Y6DKD&%?6@E_;{`2ayg3JJ`5t;n{wi~!RZa`$RVk^6zRl`x4
z0YYLOX}m>k_EdKu;n^O~>nVM+({*0itKolgf@#cIb^rY3jv^R1MSpxXG@S5Vg1!u6
z`HlMZcU;_E7p8+PFB)fJZu0si_=M$Q;fSK3<(BwgZNZxq0p0M_*nYj<WF9sPitb}8
z^Wi6#+DBaLs)>>E(&p5Ebb*_{E};bq(b~0<h0&+!;#Ct|8Lp$Jx~kEg+|9<QcrD#6
zHQLMTLS-&d`Z8Clx4GR-U9JX0N#^U#dXHcQSk(4lT5>!C93K5*U=`*vj*@>$doG$p
z3Tzm>R}rccQ0|Pz>5zzA=T=@VB33ZUQD`zReK-D3&kc3e1l{n&R+W)jf{L&l8$~;%
z5}P4HOQA9auaBK283)?#YU19!=UL<ta8tHcYhs-EvD~AN1yQla-M(s{s9GTBRjO`P
zD#xyqom$2eR)WonREby;Ipr#8iBKJ<|MTegBW_DVcJ6WPb+)zC?x{@COuNr6cDRx`
zyapq3jq?pI@5z{mba+!{6{HzbN0}7Yc)5@aV_Nm+N61N&jXzZ)<D0GtVSQ9o{F&D2
zs>DAtx-Z!&x}_|ry>m)ien#2oM;n!Iv(raq7KBz?QxM&nYj463CeGZei4iZQ7#!A@
zk2}k9Jxm#Sg<ibw<QY&YSFr7*_pc52Nqo{u)ET(Bh5tQOG_p)7kF`5J)YexsVLiDf
zcJJFLA}7&6>$97hx{O#Xxpv_5@QkpY%1Fq;6UHl9oMyL0EWJk=M>9`MTgT~48CdJN
zx=S*2S7_Pk|0H^zSh*T5jG|V)aXf;Fj(Z6dbmu6Q@+p<>qiz}JWga#y3$JGjHwJ;b
zf+#W9KZ%)8v9j9cOD~hA3=*}=Rk=5v@>D#O4-vjW|9~>fH7u)=9!7W(Y()wcifPzt
zhT!$R<ANzPY{iK-d5<Wlcf^CgDq&54Jof3WMzdO89wZUE@(2ls3ct-bKE}mQ5|^1o
zEtffNTm9pcYF~xw!>k(D<^`*czv|>~UKWC|PA~vhaMzHwkN<GqMWYm^V&ia`5Mjz-
zbviD+8b)+_Fi6Q%{#sfaoahXObBC%kd$cceLO{fn>fmXtg!rWlA<0LDRcN{vKyGM_
zpPp8a`=jC%_>qGz^C)Z>{Fv4uR?L5IqAYKn!;d{HMV;$9#A~=!M<35#mR%Uj*>NkW
z_c#?OmK!{M6qIbjXtigSxr~uey4VaS(WjUBm-YFBP<gykx0S3I1!Ry7Ed4Zxdbm-j
zDY^dNlq|Eq#U9#3@oZ_iJB_b?*f6xD#~~m$xgRobPxR>)q)1;nc%|2}Y+E_mhSUpu
z+{`K8kkpsB&Zla3{-s+Y`I%=R<7Jg`w&c4HJrhr_VAk}q2HyiN#69xV*Ht3cQdX5!
zqnQY_9a5fx>L&bOj8L$O3TXQJr%R}%h4ZOYZjFd)M^T~<K91~gfHz-aiFQO{6iNM;
zpID^z&`GCTyR}mI1#li<mRvUKGB&3vTm~qsxZIQ)SFXIAFm|Soe<yv{wz?+jh~EDl
zEAg+jy(#mR>KZAv%2!F^#Al?5&m0DY2Dm^zAh91F3K9GdKkpy&AhryL)qje;YUU|7
zN$aH_B3~i?BOU+YAYfJv`n`9-_#2=8<6M1?%L5kUilT*T_1c}cB?ANd9nlAaqZh~G
z(|%sXVXD-m)Zrw$KN3u?7fpB>K4?m6=18axcJb8+sKpy6uuZ42HFCW&fAiU9=Vp+f
zMk4bI`TFy@jN9ugiBqvK<Z{;lWsE6p{*-z1`iev7BrkOlpTF<lR851Es0w{G-E%aZ
z)nTHu$>m>Auz+>MnQku$O*qS!;IQh>Ou-p#=b2Gw?Fg<2n~=*n@gnwTacXf?7r(n+
z;iVb>OKmoanwPkNg>7GZvtZ!Z753rBtGc8VtHF<HPYM#74mU!%C}S$!`WchT;T^gM
zCLxIG5`m6^4*K)`zQdJjc{cnK5fVKc2L4fzEjK7D+g0?r{z@m9A~pVvdF)bv|BB4f
zWXQXZ&Q2^EJX4nE+JPFZu+5=lvf;aZ|6=h)L;)&RjY+V)EiWxiZ?FOuG4cczP2*~0
zC{#}~A-4|Q9FJv`^)Jf8$V+6tNY6Gd{g>Gz6~9k}Op-5nApK3QAMS`eg}H#Djj9p=
z?r)`4JEJ{e>1&ET*nG8YipSuyDNB#nff~8D2dAUJh~aX}rXDVqA`KgQ(hSRd$^9j+
z?jyZ96uI@e5o8X;kH-Hbb2BphuJ$oYrzQOs_bkC{*@?rTvvDU+DSab|uph@N6~Cm0
z<S~<llDD9oxqgTz%0{&_T=}a3o1D4-=dqD7JBJ9o2(wz6*nfdx<Esk0#ZTAeLA<vu
zUA{eE%%~k2T`0s%X5@{yO*RSH)d37rL!=qb%%i?058!=%-ADar?=ea^oex0L!PabE
z%qn}`w~ZLQS+md$tU}vL6}5oGf#}$sn79RcMt!wzR*|mYGa((?{Liz_?T_pxCqqOE
zhHk7<r#S;>9}Y12kzQ)RWdC**@BDE3j$DwPB_ef|6-i!cq8^kMBt|AiZE`iQHLG;x
z=B>PG(sF94S+<Y8RS%NEF$4gUlz3BIJbg7^-PxS$XMUTUT0q2xe_`#SZQtW<eF&SV
zvI?g;TTc6H%Nz)E$jr~NPCvj(*s^~sYnx_26oeSz-jF&qA~sQbyT~$R^6tASg13FT
z=eN@)#jCnA<)2t@(GoX3*QQVOODh|yBNo27wWuW!A=3+RT~jIBg=$w5xOLCT*2RSx
zh?T!>{Mp;-JDf8JGFY%>e)zepu}xjAZPdHdc0Vf4TSD%*imS*o1b}iIl*&2BE7Nq2
zTHfc*EOsPa`jIHg4_9_kShf%zEDdD4<fN;d-{Kx?8;@4i{N=_DZ(@(>Q(y)z{yZ!J
zCxjW_x!q}Elnf^3Q9sd8leT8NmA`V0zuK3juOC#Oex;d$q^z42AhVYli{995SB%V=
z=H$0A;LdW1Fh#`q^Q#I!VrPn>jG^E5dZpbL{8CLoEB;+3c%h7{+R8fw3<Ru!a6g7A
zqP4(-#_Nk6;|aSxmOqr+0+7qI7ofTpzJZDC;fP5`!?u4nEV=yWU9eQUbEw)qnq%!F
z{NxQuNjE*+*#<L%c#ZITs?3Ls?Dk8BT=LY_OcRqjG)H3}MtpjcO8F+P>S2**Lcuq2
zjlPjQecYfV6Xx{qpWcL~rl-C3%}q7LcOt4JrhCx(0osN}2Z(L^)#KFT@EZQK?B3~1
z=DrCav$x*ID3<d)A3oY3CG8*lRnor82SmREb~0y53JkA(bkffYJSKQQW_hBw@RUp?
z<Cc33f@CnOjK8Tp{l&FP0aeKw_0D_y_;V?xEbcIqk`7pv_ha7b8}lNrGFq|WpJ0t^
z_Wl~<5rvP2sy*XtrGR7t>#-{ud{_YSJZ>QM)Cg?6evf=l&ddH>r;CYkAa+V8$4WV3
zL_6g!_AjIFB_2p`z;RthpLdKr62&kaK0F_H6Z)IA%Ow#AxUL|0Cg?svK@-c}SmM8}
zl-nIN@2=C6S_#XP|KyQMFfxR(1k(c%c#aTj6Bm{mWzG8;g{wFYZ+^QE4%Vq|(yDKD
zZmi-eai>g{7$A0FIyqmWAweQ0X231na9V)~9Y1m`;<7eU{`L$BxHwm%F>(1vrSlt;
z>1;#I$@2;(`o5DLr_5~o*p^^JrLaii{a$h@gQrbx3cIn~8o}RM48G7-NBalR1!hq6
zy3xbED2i#>Rd_Yx3{rE-p0_tC?w7tFE2qH-m;puv3;2a<CAdjnYC6)=*|mNCxnnST
zq^d$yf&gE%dy8=ZtFlt2?2r;`qpl!jR&x6z(E%|Uh(dzl;0}4b2Odx^^dCqatD2>0
z?4Ne<@Jy9x<{*|t@$i!|=2TeV0e@uGVWfwi$@uS?D7VNzp&Zm$50=mYxHoTFcEw|F
z<U2r&29Mbj`$mQ6aHqf;a9WA7B+F>vVt=nunX~%5Lid>F7;zlE&uDF9^rec)kPx4c
zno##aJ<YQh`hj8JDtWv^ted`G{-i#csH<|Z;N+QrQH~1L8uE!?P5g-TQ_L}PNnmwa
zilobzx3KqdCk|ZrRv@X*N$D{BHeqADU!=kTdg~KRqr%-t*!W(;MPNhx_!nJRCKG$L
znDVI#y==P0X`zJZ-S`Jl&gijYY8ks(gVyZeM*W)Dl;oiwy}UUfDP<foGHqVnZG?#2
zN0ZNTbqZ|er6yAY<MH2c!h0K|3UX^oZL_U3h-41-KRka;Um^I@H9c93%z0^=La9E+
z!q4h>)^%?-(_&GKG(K8crKT&z)r&~R?wz5hl@wp{b1J*hNi&1(__WXO{pgwJ{X@tc
zj8X0QS6IED)K452>f-d3k4xVTDMbzZrGyI&Z*XPk*A07FJyx#hyxa+LuDh$rYLR7u
zx?PL+<MXgRh>VsI?QQLOFijcsmRHr+|FcB0*r>A?ky`oAcPp#-f|Ib2R;$7#2-yLf
zekt;sG%ZVSyyNpG(VF4e;k*y>u@9=u-av>J7Ksnq!nO1N1ZDG9>@iwh41S?oj18Sv
zd>8XJ9NjPUQ>-U5?8>X#BVI)zLf}<g6PrfQ4)l@fc>A2%yYMdRk3eiIiq~k47Z}^4
zOea7Wrmx)K;BS;sLKzz$q0Gv=@9@DEMr2%1{!r6hLL|XrGU@4677Hr^@~{@b$WYp9
zOB2+{9TFt)Gx_64IIjk!VP>A422qBckzr*6{O_k{=1n=?^IXV+Cp0l}0twqwh8#MA
z-;61No%K6gc{X(*Tmdfr_fw)m`Lag5Cp*i$h%9DswM^gEx%16BICj~rGrx&dYcZJh
z6w7EP8kO@fLT#i5(3S!Aeq)jP(98)onX*?Io-#PpW;#Q|jGPFB0+<LcTG{3NsHRZ!
zE*LyAlu9YYZ!*{26*apg)04>}nPRn=MDZnaQ1It-`Ljgqh$F)7Pc&0WLDvxCPP2>V
zhpa@=dpUIdC8=92KSJ^LZJUfJdC~ScC5F{c208JjsMpkVn_zey;mUdn2DO#%0}{a1
z20gzxBp-=toQ^fGtX-_pdpkr2YsCoGxu^dsQJlHrIb|%?o-q!*CS05O;^pxV`N!N7
zR!_(Dg-nh|1Fx#T%XHn;iL_+cW(3M85>`IzP2_e%mthMwi2QOi9XHyHRIubQ98(b>
z<EO7aOj^3BIaY!14e(Cy8!7=@G(4C0C{;LmnfNP{yU{$tJg;}EnGr#2z&8a>Nc~*P
zzFA+`U}-``>n=Jv8w=Hp=a+YtB&@7nLti>MRkca%6ZmhQkDu1uuFyCuuKl94iy5w~
z`dat(6Kc~yfDa)f<=nS77%%X)x9x3qWtnQLF_decc#ub^#w<OImQ-lrlV|6aji%G|
zjb3?6L)19Ytk&#ps0x-xl1o0J>-pOV1+%lX&7YS3YAtG%u@BsQ1<H0a+PmcYa;<(!
zeZza_VExUdT+TU9wY*Kv!!^{o#3^rfJIe@qox><QyD(@pAz&u1zc#PWYxxf%>4%i+
zJ>f1wrt4w_Ub#N+(B$y+MuXWZ69&dfwb@v91YZLU8BJr}-rB`mde7{acb-8(Xqjl<
z=tJ5TOlivpezEs$;Sw4Vk_Q@?XUGBVI)iMCS)xs+jmfC*6$?3qM8xFj$O6Ubh-xtV
z&)Mo|N2bEnP-j)y$4?~QW{XjMSc8MR?pm@O>zTeci?n+>CS3lZ-mS>!`?8*qw=yhw
zFpSa;%*fw+8C&pybA*z;_N~sQ;@V9ltlPG&JmY-#^Qs9gEk7won0=p%(}|#uD4CF`
z0o##%P=R$2{?EOuUrz>kqetqR;B#?wJQdq3-DfAe2v*2TfN9~`ZY-{|?g$%OdI~r+
z)-oiimanGt(+4#EhD)dvMoKukSy1zXPg1M368&5}ds88{kCH9+m%4<dC_+IvTN`V`
zguZ)Ycb1~%2Ap0dxpu^Lx@tTCV3YLLH>5?2l+rI>;o>V(;nc6aY><>%7i};o%DA-X
z1Uy2wlHttnZ6`+CQj1u~NBwvm9R#1wht{n(%>IC5(CU-R=(p!ISmFG-Kvs232gvK8
zTX77?%%E?UK!+J{8-%ud>s9=Gso~f_Q^BL+RhmfKl+m($Qy{<ij9RAh)dKdO%Y_VN
zcxu!q%yX~H|MoZx(c2bRsO>H+LopKZ+OjRYx0I$EgrJ!c(fLZlGak{?sPFPmp!?(P
z&QSN(&~TMp0^LBk;pZhV{-XHr-TJg?C}ehSauQ}Tqs}a*`{GVDGm`2Q$zMtJKv97~
z;|DgeC;p);Ox8Dtb{@kITU<gv_V)H3Pz#P-)eB}0kWkKlXVpAwGeVvduJS&V59$Av
zp78}Pmp&7yM7%K=?~kr^bBB!@50E|Hc_xAdh4v$7104q~5&14o#Jn;qZ~qJ!Ks-@f
zLvpD=vXmy(Ap{YF^Byvf9)88R{h{!6(Z9A~meMZme5udCBQ|oQI3>w0>~wPW3@*p{
z+>H_ieRSdBA|Sje+k}!HZ?nJVp~hju!+G&3iV8a#SRM>rxiXa>IQ5JF&9FedD#7Of
zYdaEFj&iZUh)WI1<Mst-#&M`9ilpxQ^JwPn<#nfjq$Vs&4dMciJft=&d{g@N)i=O-
zh3a*%b~B#cGocAk%~wr)%ZEJXYcFO@?R*N>4!EbOpT1^r*f7L^Yk=Tz4i6v8z}^#y
zOyn+7FT<Z8Oy;x6M<@bWa&5<K!-!myKQx@rci2`bCd#*0G8|iQr5u7xfrKvEe~jqH
z(OVCvi`bRFe<@w8SrV?oI#hpT%a{>r1MTz?@sd}_d&7m)%rz#3K27~6N4cj}L4hF@
z7!BAz<gkYqfE7|tcPS}ssjrk?WGoYcjxs|y904hj1H`8c_>ht!i;4<)u>c8H;YYk1
zB|z5RmN%O~5RzsAT@<O_ht~C@$}?g;ApZoiON!vU0osNX86C+6h0S5^gsX9fM5w+h
zeZ)$73uv%N!fV_VcrA`2_6zJKwBmR1`TZuTsv|S{qL44-fc~f&d?8Q9@<Rgs3j6me
z`b)i<9`os@dy0zV^S)4)5u}J!qD%rsVd-N9Y@_wDSCJX<i<Z#;Rmy>{N!+@W(`Gc^
zrv4K@!z%-9d)dmx`K%w9>onY;a|X13GQ!d6f<#6}W^>M_NSi%2IQ*X)8SjBv;Oydr
zlNxIS9Tw;Z^|0M9ss|YrBIeB--#wOlOQx=nH>~ax2%n}!?U8JL(77XXih;`zmEzy-
zdQELSOLU>hv3la55SQKSO%Y4ft?tI-ft@8<jfb2J(m>yyPCInY*MlApxPB^pOUH%v
z+E$d0l^r}{R?uR!jJ_M4ufTHvJqT<o%80&EhSs?>&C1et#R`KKau$*S6NFbeLSc)m
z=zw==ikEiC3eYUH7*oVWiYFuJIF3sSNCjd6*oqLuryf3!BmHm>v>>Lluchc!Gl63_
zahQEi;@rXvdM^5zc~UBfGsq`scHYmoY4(Nwx_TvL%kv^Y)kkcg=9E1wAXA*%G6Y%w
zOP@^*Ti&gpY$C(RaFKLdGC|lKJBa8;vT|--))uN3_!oV<<~Av)yV}t%R9WEhA3rwG
zpLR#I$XeWv2#^Jm7Fvmc)Hw?FO$OA(WVd32m}GuS>I0`7@%?MZlohax64ah82*y(U
z{!#w2@A7c+)?=Q^^VhKs-w~Ye58@OQEaTvNL5V0{Lh(TO8EBl|{x16LE1pb+fm6@i
zU*J>;;5#1c+;B+>X$-j*K(3;YF0XZ}8XygN(9$IZRwX-Sc@Jtu1%4ZdXvh}&B4t_!
zm*|M$kO#f+FQ@QD4ac-tO2$@D*Sng3EAB-!d@p5E`>fxhg}-DP2wh9bOBGl?O?Ux^
zSYM$WnwG71Zz~YjJ_U#`)$62s?&gXH!u9a*0l+2LibIBR5Razt?XfeT(7mv?(G9b~
zO7ajJU4Rd%o`S(7HXPXYFPz*Yipa3;(w5@51yC<%b+p|%_1sWH_v;g!8qrtub`J9t
zc1N<ru*AL;20cI8cJL{l^E1a*uDN#wBjHd>$}l*45RBsVSCWgps-JwZw-aefHggIv
z`P7J4jHW;;l?)WHf*#azmO>CDWfg<0U|Dq~5GhS*7L>w&23P>!3c^SVlFnhxeSn(y
zEP!Aab=6Z7Y4a_2zO>?o<4^V2#2*wq1x~NNh)`j9$Q)A1db{X8d^;aO!zc*uPn8q6
zpUBGW-wvb>)T09K*NNHyMkDndL}dzB&kkDnwM`8j^&s7Z)czweiF4hCh1wHdVnQbO
zmoj-$dp^9ZLg){e=}aQ7u^?$g()Q5Vc}&)~0C~PC2YiE{mcJA1%JnjAez!k~)4Ml2
zivFqkYJ?-~VzA-5A1Oe=yK!BD%PE%idywVFJj>IRcF!|{1=s(vYMlz2@PyCI25d$5
zj!MDZFn?m+6}Os_MLhl@%@9KFd{H&Cnii;~sqdFqOvQ&E!}<(3nN(pV>=WKS2|O4}
za*M<=b-f5HsBX9UEu?-g`0r3@Bx>*ROUS=Z#0&fEIwCpO(E>R7sFz+Z1&M^-f&WKf
ztBV8Z+Y%&nX&>2-?0~%Q*k&XVl$Q?&^rzTFKAa~=@g;$QNg;z~@lHEA_|5&exyAZ{
z7>YwFT&mj_`oNn%F{Dqq0pMWJYk@vRgZwL)KV=bI%!pjo04Jw;Z^qeynJnUz5PWNI
zL<Ia`c28G#A}*~<NVtFdblmRY@Epc;tZ09fP;ccShqHmUzQrRz3TK3sQT==R1bRh1
z^MhL<8}JpY9!6fMg85Dm;FNQ`@up$|3uele^@?Js?Jl03-9v<~jHabO&XBU896kx1
znO-S2`72v?z_}^u#xNUj4z*Xs|0r?)v7DfO@0P#l$0Um1QlyYI6M1sBONNnH<=_`s
z<^9J;YqrlEMt>fIBVrkdckzbgDd0~^y%}+QXe*JZ7sfsxi}h_XMfHeD`?eVYQr*w@
z7l)vA-Xl8mxI~vqAM3vup~SR_Du0xB^MXezp*i^Hx=Y+32%>-f?rMt&I<bbHrvI^A
zYVhhmd=R5r9O}_pzuwB5WKIh@ADmGjQ}4MK=!81%)X}DwMrN|V-hP_l7%Ee=`XJNC
zkdyWqFe{B*S_)eO2!+{6{~jyUgT0N;%-V<Ygb7)9qB9YpVXTT5UT_cowr?TM#CtJ8
zlUQv8r>WC=pUG!51+!P9Qw#;#8!hheF2y$6P{__M-eIA8|0KE(01Axh?%*AH8Quie
zo!g_dD;8uM&bvV)XU^B|WaW6!|NNNf>Sj>mwZEC{;S&5d8jcZm>YUFKu70d_)cXG%
z{^nm5qZRtvEy{opVx-~MF$w0uk_@!&aRcooJLDSi+tK9$G3OVIaNOG7hHJ~C$)8C<
z%?LDB$bmWE=RodoU<d9O`9KnpYz{M7m|IpX6-yZS!TCQeBvcISXM=sjdJx;-FY}+e
z($=I4!|t9Qc5Ki1k7uYzq#RV-JDOE(T8p^iVBL>B$os>65;ZK*2rWh&oqKQ>#+zc(
zWdejmfafQ#<x+zWO$`MiqakXuc=c1H8!@1w+0T$11m0%fuwq008%K3<C?2<(Jh<nP
z&b_U_0bWZqQHB)^M}>=s-jlmu7Xm!s*SOqsPx9xe`$4~|Vu`-0r87p2xC=o6-75g4
z<-H^IHY;D2|37!y(xM?;Kto`5uWH{umLr~*#DKxHj{rI*M1pRl03-4`fg0LH{r@+#
zR{`T5Y46jec;EBHM-71KGl<U!DpRN$z<aHNlCs><Et>nG`FL@x@RpM2WGtZ0OU@9V
zq4@ys*2j{f5%Tw*qF0W>42^zSa6ag{zcD(jSL<7YFUn*@3xpQz)bimV=%eS=d`OC=
z53m4p$(f^^Z-DuQ{9}WSn&&~cGH>CPi&j66U$pq&h(;{mqc~Mb08B}kO;DLT=SSyB
z-S8K;VtyU{cn*-U*5FuOfZL;-q&Ogev$;<f?+R5*)b`jeeL|jZ+o*90M}>#T8x(N!
zf^H{JIk?%^?yx(wn@FA^;FFfgBSEeg)<HbhIb|O|QHI@@Y%sjz`a*6!Hf(!(bd~fJ
z3@!v@yS7E1({)-xL1Alom6TbUcdrI?Tu2VMI2mub=~uvaj@F;DmES++0v?xS^fC_f
z-UA5Gx3jMn|6alGe_O6d3-Yl8QO~_XBtHLYqQFLc$|n=SLQ)xWFZZwNSfbOzRPg7(
z(v1wsRXti!=QdqL@n{(G?}kqxs3|BU?iFEuQmjgf*<zd#^AVUY{0lkf(To?0;_DN@
zt%%$!%Dc~s>IC<TA)+bzdzd-pSS{};L6!adU{UKuRt4-?cCu>z*8V;_Guc6t7h&bA
zZ|6v${fN_j13Nn&wPgPBfZR>wJUtzq+1)LM*Ql;^VO`x25KO;)6<p>c)Tn<C4}!30
znkn9)OHt|gvw7m??*6QKAQnD6K2CF$X>Z_dX0^1~bUoN#O5^zl#q4oWwuyK;DI^HU
z;ZRZ*+HoL6EdyjF$L9k~e54=P<2=&PGHP!Dgy154cSv>@2(R+HK0o={;)8AsbKjk)
z^4Nb@jzCLhdTqcXCYimrp7OVxYU`C6v=_cOD~i~Zz56>@ZQezQioU(QowPp)lf8Mm
zJ6U0UwlhBG)IqYD->pO)36jc$^Wl}_W~O}G-=#|lPai1INNc#g+MTYk9rc=C_-WO5
zIUM8P={hLW!*aXK;&Q!TkMp|d?d4R@i|$~u7rhf@MnF?4qxuIpT8^F_k3H97l^eEl
zqD-pGmX|-gxEhXexx-wX{2~%K{Oaht+B`NDE=OYqoxt`bx_l2~k!k<m-J@8(=r?8@
z9p8xF2uopl=71Y#bnCSPy5+*;$=1>Rl;>Rf_;^iQ)1?lY<L`w~Qqh`PYd87RZjv8#
zb#>)=cfB~+{q^y5gEOn_?NJO?1G;H<YikR%lxkQ00a!GU^xw7kQiH0`>wU@bDr^mB
z6Gn{(t=FK2<3BuxKsArR*hOuH-`9(l13w^Tj~m$Nx~aK)keew-Wd?o3`LF^&tXWR)
z7{ucL7+S*2Fc1j3a9_;BSi}~6Aw2r!c_k%Lg_+)jf!3K`Uj+P*qU>kuzE=33mF&$m
z#&c%*#?3k)z7G1KT|slsJxsnUR9Q>bve)#BR}=LO`*pMaAFg4GeqslnJyIZXrzKDn
ztw5${S$%yn4nAr6&(;hnn6QXQ=U1_X-d&Rm$=2h<iu8*2j^f!Vogdmg7v_<ou)C9O
z`;rMP*5Y6@Fv)&>>rJ)oG<u$8+Vjt(*r%CJCKxVNwqwhgo+xl5aCa<F?Yye=h}Qsw
z?4mVAX^73^B4=vtyiSLevF_KB;|mB&Nm==~rEpD64d`$yH^v2E_T+LkMyv7Gr4gm0
zCDV1$cIW5P$T(@@!#8Q{N&*NW{aEy|E*G%v$2t74BmtS#_CYTqu5kc;hkbn-*&9JZ
zN_rg3auc|j=|c+1)h;rFHt)Va0K?Tan*}H%)NiCHBVP04Cwo`umE4>b%CaY_Vt&<S
z`JEd&AofL>eGmU>YH9)*)Ywp5pYNx64&Gv}4QsZ>PA0v5e71LXavB@q8ynDt(*m(0
z6l9PKxzjbzV*Lf2|8AT-M};oEyikOx^>4r9@iJLJih$p?8r9%DpkC7FQx?{zCt~w%
zR9V+^o0piyJ3mmJ{q(P^v)yR~OyIz0@%nN{zwIjZ`@M<@v|fTMnvV&i9`UU#_#CX;
znO@I4n{8N6GP=C%k=1N8E->Id?xfN>jL`BmC-j-UlyfDvFW6FmQb@ox(nOCIE?O_1
z54PN44rEuS!?Q4Z*3->ikll&0)#E;a%_!3wFevM0jT3M<OSvo<u6xr3YM?-!=0Yz3
z%RV`;Z_Cgv(K`wv^9>v?(buvs0h9!DeNpW&-xRTGTcTICvXSNpoo{kC_B$J&^O*fq
zYFNGDiy>r))F6iU3qKaMKkQEv%Q_pxoLLB*#J|V6DcMav`a&~5`WhatK3?}})}Z#I
zZcAUvbIsFhk?KMKS~656d3J&_LX_^e<!RXVajKt)hee`*WWqg!OGth%wzzlM8d##g
zJ0no4|Hs8EME#8n4+#dE=WYhs3Yh}Y9N_rxpY|3h<i44xFqL(lZSfJCd=Fy+Az%kD
z_9MDoZbQu3zrojR*hwiQ8r>xOllY8GPUd9qPGoU<u=nIyu3j^7BMyPGtKU_L;sE};
zK)>;K`)p>wj;AbQs{AqDFHYctV~xerT0p|fcja@EJ3jyn+Wg5Dn5(mwPEY?F_-;y3
zRru}bWUY*5-HI$?(6HUN^P!4B=u(c)K}kB;a@`0R&P;%@&!0&xkr1x4?5CZtI5?cS
z+5keS-e&O)08oeg+V)mG8=9H<Gc{$<($`=H%qT*O_~}F=IR!xm;H4?P0tFJ0JR%Vm
z4}e#%#@)fy;pxU4@e<pvN1o{{H%s`C_ak^bA~J%I>&Fl&bLgO!wN%sh{BRZVkRtqZ
ztECg9enEAt)PgrbzDr`Ao3CUV1x2hYbt-_;0m9Y!qw|_Zs_pDx$_Lr>a?}(-7F1<x
zbEq$itEGg35X3x6G;MmLwYAkdnH9Wg$en0zW@e`6`*$K90TC2@$(>jXd$!3YA|G!3
zYyJQmjXhQQA^7%}A{4%`U;-dt%q{-eT)y@*z^r!PcT2!^fpDhUNSfJt`lr<yzdVD2
z%{}~49a1f6LuMVJZMp}k3JQ7tK!(lr!n6cN0tSzW12<FvNN4s>OiM#2@b@G->R~vT
zQ3FlZp%(+BGd<I}Z!S*2u!ABPg|+#_gQ;o3>V9-<_JX@Q=l<&6cfWKQ{sc_ACIi6S
z|CW(r!+@Ghdf+8fO-yx9=+CTCrTcK6Eny58EcSKNcN`STLA06)dL9R#H$3njpoPqS
zf)2u`A3<h~eac5s%%at_b{9-%<tO$TTR8=svjRlEb?4JB?4bJN6~_=*oZpQFMC$dm
zbbZRSRE*C)*~jaK4%C84R`K9BIG<>>a=(2Eyu}O(8_Udkp0)n$+*!{r$$b+|yG9a@
zjR;(SiS7BIp?SeQqtI`b#7)Z^-G&95U|uA+v2EE+|K;C8&_L*U>voOTOyx-~2<+`6
zye9)luP=L$2N_E($&<GZ@yMSui>@^b*G><v8$b=k=M6MdhO^0rCx)5kEY{=Jh6l5u
zb2tbF&)@T=9v5II0VlN#yHw43P=JxoO+B(=tRkVv`62-O0jG@LPfQcuWhA~oAxfwk
z52C##A^!VCCW;T)f;Mrw&<{+0cw7EB716eXmaULs#;L=KN>RLL>Bv&<ui?aOD5F_a
zpkEKhN4D@5hlmQl)f%b=pWd(C+-STmsqG01^g4UC-z+ul-(NR)v0A%a-6r91DRz2o
zfcSHjh@?V|47!ZG_{EFi-CdR=t(@NThP4p_>0lr)HF_lAEeI9bEyx;}z+m4}HqaT6
zH51-kH0il!rWg_(@-4yU{Z20@p?%g~pZ(Gkv#eT-d$Bw~2EKD`K6ZnlnTY$%K31!|
zAhq-5R;AkhtqO))x<a$gw0h&j{h|YH;(Wn<sL%NL9pV0qRKxV8C=4^~&SQVu@XRpN
zhQ)f-+E9zuB@jd0(1}Wd8)7%mG_(yo&wxPeM5`xqV}jBGHwn)7#XdS-lPtAfz4ctk
zux~@--&%coF#8<Y>)fG}ExheH=tJ;A>*4v49z-p5T6l?a={LOmz#b0Ag5k{0VFqJ-
zY$tq^IA?pN&kZNuw^+xHohS(+Y&qMbv+iC4iaGj%k29tJl=$%1Fb2CR@Bl{uI|h_-
zR_;!CT^C_Cm9O9wy;KiygbSHyeSK}8g(EtKxOJWCshBulHvBBmgxAw@Cp&iTjDPqI
zP@yl^o-*x+OV!Avnf;9F?P}?+7X!tEKNrOA_lMwYx2I&lK3gUH|63il1PSCi9lkHo
z^XbM8;tDZxpL3o4Si-Mv<#V^&V5j;?s9GNCQBX1!g(4&rjMe#3#&!#R;_Mbt{_LGv
zB|`_|<E#cnX<h3z+x~(L)bz!4_Z&uS-me8w!D$fpgV=YCafgb42`{C$2t#m~6;y`Y
zah)2FT+%ASqie?_uD_Px54C{KBju3+E*C=;>(5+70XNV20a_CD9Jxqk{I63#!NSQ@
zUUqYCeZ8jl?mBMN=)m)8kI&50)td&UREIIh$$5<0=1;y@L7;*g6#L*jE9h<u?Esw>
z|A!1Uzv$WYHD%h|3_0%#Jr_K@@8`<tc|e7moal%p*>A75pMN(we>doS5JhRM=!-zy
zEgK%ddwIWus3fo;J~B=ar+l~{mWuNV(u-JpxT<Q=^+Ne$%p~(gH5up*+W>UavBu(Z
zF*#Z1!+J;oyX$?WdPDSt^yVDC7t?DGl(|b(-4Qll+dPC%OD}Ezc9e`~rmNWhgn4*L
z`R@j9kS7OfFD7sjORUMqi+h$FP6F(dE#qd6N;;ioxaKf|L*~#7ovAeifD<Czp4Le8
z3+d3AB<uinr~T5N7^#i(3!NWoY-4Uf`U5Dlzdv>Oe<{2lxg#sZFMr6`^`i{<LjmZO
MqNYN*oZ0993(ejR+yDRo

literal 11846
zcmdUVWmHt**YD7ZfP{oJNJxW2w=zhB4lOCj&>$rZGb&vI21AOJG>CMKh#)eA(lK<$
zP|`8?jQ@AN@4a8{{d^ZQXBJQFv!A_xJI{%Eq_06q&P)yhfhe^!Ay5#ApbGeMULyrY
zM(*-C13z-U>Sn%%o-kkkr``@Ay{Ep<-8_BWob5PYIC%RwdwPIHfj?nRCtu&^KJsE>
z?*F|&)YIEh?B|F2On}Js=bGj|AkYmu{FeZfp2-9PiPUI8?i&SUZes#{tsIf7=Q}k6
z`GM~hHNBSvzDek3b|Q53?tbLbrH?E$Nf&Ig_4B~Ge-s+j!{}q4SUNR%TCJ(eU??!C
zZKL0hz7@j?8&vOeB8Ww=OMB7M^6M`={$kgYUf|<fcsZ@opDf(_MNxZ|_4L?WN9Pan
z=sf$*PEL-0`#EL>8yy{Ol>Tv;lA4A_j)PpBhNf8a|M%-}sElp#*V@`Z0|SHN3@&cy
z;}<(Kq0^Wt9ZX_PwH;AeQBjmt^NV*)x4F6Lf1s(}HBmb1s6}&gLz8dW{@<=9TarQM
zmeL$4H`)Btel1;7bDXW#8kIKoa+2+t>~h&C`P50cI3AEblgv}^=5gm;-;nhO6j~cY
zLetnOfD)y_>f>^?!uETW7lw8(t|VRx@XK#QQuQUbiRA(m$?o?Dir&0Wg*IsEx@?Ii
ziZN4FWPW7I7{vskCD=3>qm3am8hfZk1GVAcf5ONG1wO$G!H-xn8_K0$lH4H6Z3fFG
z7gqB<<YqR{meGO;#uB<k^cbvaacHN?zp*=;lSH;r45)yAp>hRY4#q@M6SAx-^3yT}
z`ft!aF0A1islHb1#{T_U>l@n`C)InY{MX{e(W(3cOcN7hOt4S8uL!92oK!_AV$=ys
zVhNs+4mG}+40x?OqXi-Q<5d+2H=^gE)3>sCnXH)|6Oc_Xa9>UJe3Dtd$;E8e+`!N;
zQ^a*6=Aq=7h<C9e+*sV7-$(NM@Q|Udl9IZ&XUgB-lCS8Txe>{~R*<@@nvQCq!qVIq
z?(_+zB~I&~KiTU^-4A^|`Cit9h3g3$btjRqN6qYY5E=MRDrF4m!yTtsU%r7Fjjou;
z=UXR|m#+-pywbgA@Q&ql{f(3KU84ZtXELUcEB(d8A+FzLbRsZzwFplM&OS*gRmv(p
z@s2@+q<BZ%b<Hv6BuVw?`X6g%raFVHx%O5fSxSDA<>iG<yoK|6$vUqI%N{f+z#gVi
zB161WtY%fTJq%J3E#JB+cd{iK6!y%u7^T{O5wZO;qFDz?^N8I)8nfzp-<xhW^8Bj1
zlJ>_=Msq1B;MC8(=&P4J3vC?J09(e#TIu_<zyYKU_&8F~OY3eWFx;M&iIkCqxV&Df
zMjHDsS{*sc3HWdOp|3%2ZSUoC(Y;~qzbBOjyBL>5t}FJ$D6Y@5ZVgBrnUTY3=Hxi9
z#c!lvJ3YHb=~{Blg4@@#tf;s-J+fbLp>&~h(w<nt*ppib(RhyJFh;!C8lM+(5b2y{
zV_s31RYo0ZEG~sBsCx*iSQsz6BqU39Ihmr?CgH^<cbJo^O1SPz((W2mY}y&C(TmjX
z(H<{!G$@?LC?XecM+O^Gu<Cm+p@_ekWzV<Wn%87!>7mgRySUJ_B}UL(kKd?cIxW1m
ztt17RT<|Tc<aJ8;Gi;@YnG}5anowWV3RX9T@qgRI=BP$*cKd9(?S2Z9wQ^#-f5x-D
zbLVP>+LZO?ZT{=1XBiT8a$UQH*Sak-A&L*=S-ihTnlP8-Sz<7qy(Thm8yFcsAZgsA
zY2BltHMx<ZPa0p$*q=Q#q@XBFW7hQfi{ep!ySRI>*xB{q(|0aoI(<9tXJ=0Ai5(|k
zL(o<R=HdLfl^F}Q4IBRu?W0*^y*ggo!$SEIQ+~4%tM4UU8xGse(Zad}CJ*m5zOjmm
zJe`W7syZrGyi97>bo*0GNZTiIR!=P1UWXi;`I2s&V4Qz1Fp+}45oQHWU1v=gsfPOJ
z=F4Z407{PRL-p}{<RE`D$^T8tmz~LYc2*#%acn@#)&riA(c9)3f1Z5-(G+J+Ve2Ch
zNArN_Scy0}URnCQwscjs_m{e_5pT@0@loklQy=A5Nxsi98qv-vF+Wp=sO^wUOgSry
zv*$`8!C%}?{1OA^cd3w4G*4zG`dO|5ltxC*a9vPl<gYjAB_^zngs-^PbH(nTxjfbL
zZ}#p8e7eJhtQLOSuzP^vwDQc)i2r!!;W%@_`WKgdcbX=fQB^&w_>lzTVbdR<^&q}M
z@VC5_T)*Vb*9{V@wo8YGZ_2wLUYBkwjM%8JuR2=cd#Q@cu%YU!^-6bL^pze%^;$57
z=(-5^LOf%0iL??q((KNh(yM1~Rp-7LR*hWCH<V)Un3sIhh2#dE$v$lA4=C%=l)7CE
z`)<;`dvpzx!Q$)`UDMi}OzG}NTqGx>5793K303D-OX7Apa_(GwV>;xrY^+66;w%^w
z>Lp#|18$di#6QL`<=Jl*cQbu=(|p0%yEcD|F3K?exR6QVZ3o#Gp7TEe{aUSW`L}NN
zEVJFEFl^Gh_r&v}s^{ldV#&kd!Mfb{N0ha0txfN`<g=JLzfhBy#WW~1{${-yHh27b
zfJXwZ@_BUw7kndGkW$3>6DPx)HQlNb@V63{4E?5o1AUmd+KN*o;Np7jI2R`7r)_1y
zzIdkV@;&=1rgQY1>L?!EBb$l}3r4v4a1FaOmzo6!Ds8j4I%A8uW@uElwfVR|5CXRh
zIkz~!eW|MwBfxLvT6Xq`zrL+jXTJ7<d8cNX{u{Hbk5`jtsn{DnTod-@e>8^T*|fdZ
zkP?MKMYmPnatABMQAY7Z@oQaqq})#=qE28M8F^f$q-|(G8PiX^&EitLQ(RbGlx|j2
zB5i(p%a-@~#cv5d6~DI-hKCA7%s4kG&%p0zP#GO<o@fTadWhr8$W4|t*vMAsQ@*92
zK8styF0ZS<3y|7mb|x-IQ${TD)rcb<E>xd(4IKPfh1_}gh>q)v{KHF!5hH)z&laY7
z2a(K8qMn>j@^CyozdnL2V0lUL`+xbZc~T;-gzkKuu1!}7aqutB;B^p;CbO(7kLj(G
z>YG`?vNCJmQ&jA$X5_y;t)h-~iZt>}U)xjM%T=t7VbxvIRLYTGKSZN4xI0JrKCf#(
znl;oLI%7^B9UE+|&ek-di_G^n(uE@@q7VuMWZ8r>tJt9e_mRfixNX^UmB8r5Jm*A9
zy7{i~nq<><w-IE{-Fs2U-$N!k1{NZgVf$`c{*JaG=`oAWK{*+=&%#rDZPG2nBSXR&
z6teFy^`x?C-8=G4k$AY473hursHFW&FSDdyR*%KS30quX@5AOgI_jhe{iUx?oQCux
z3@5c*#|nVYb5Lw3Q|-wrAMyQRVvVZ}>hm-HfW34$V$7u4{<6ccR=xiNvbv-i@F*j0
zljFX>E>nAi^E9BW6AE49xwjUhpi5`q*O%r@ecyizpXQktzA%vZaV_-6YD6tTo%};P
zz03_iV?2_Za^L$|Y!sL7I{eD_Hip3I)e$b>UZ`2wg;UIw&uF5eOxFN5DAwuM`Ybco
z`6#LK*<{}^ui*Ojhj$+<_#R^DC7LQ^4<+Gyza@#pbnkst=^6iaYmqE<=^PAjKy1j>
zlE!ynmrg+!PY?yikj~J4GwF^lPcWRa`jOtd>1=JIwi+p4Vt2;!p}>$P+zLD62+JX*
zA`+P6$6lq<%4~iOJ26r*;9y&{X@h~iWR5PIUqKA}_YMgLTBUzQz3?3}`5=36BSttc
zvXGckf!1x(YyYPzQzQk4p2Y42j~ZEuE~{?xRu*5dq}r7k1Iw@PkFuwShT<jqnTS-q
zCd%us{g(2g&APsj<uxfY`nb6wwnD0Q*jT+dQC(6}V6~i^5RFbpOd$?YcXuhl=XCzK
zslcS>8x1#XF-?BG7~PN0DHiw*+B-Xa6N{1(YtEvypy!79%n7aaKgPcnBFbG&M;|nQ
z`9gAE)xuQ7-Kut%jDPln-s<gfm7ZP1Eqk&;CZ#mH{awi`C_|Sw<m_NDhx@qsmfYMU
ziD~vqkuuGc&FDF{$9ArC!!{=Bha1FthhDVR*WCTQO6+JRH|d7?KatDJsjCw+?aI%=
zn{N`PP1GYY;HiXYOglx$ZE{yGQU5yxT~Kqr!W%-utU<PKPHqnAE*~g{2FKsLobMeU
zeq0nD{l%`6mPWo_@=s&I-rMs(_U4gC{DIE1#D*GBUAAmklUM>rFOAM}u{`raDA$_;
z@hZY~5S27@5A4o_(Mh%om1Zr|@4~wYQeHsV9p<%@H0f?=YEBk1StrMQDAB%N^&LeC
zGf|rtyR20;uNV$?`uw~n)oHTl5+mNY;ga1bVH@QBwvWy4EQ;?#Jj6b}c1-fiQF`k4
zK1>f442jKxo&4Y_Zj-bpBd_7Dd1dZx&L58M`5W;Xo9|=ZG1&8CC_g=wCxb9ih9xQ^
zK_lb5r}m-Qn0fbd>Bji<<AK6N0ABKA+t5ce1S{C2^a&Zn>|--ktXJ$ilSa9F&}&tP
z?fuEOdxmC>)ptf{r0YiSwm|Np-^TlQ#`t$SHyka^{}pFT!5XBl_rAgE<eY5irDp$3
z?p2tWe{<pGD4*dd{Ej!P?@dKkVSQk9XkSD`3QJJQddnw7f|%>GgULvpE2x@~F`y!N
zb2!C`S@Dz?UsPnuaQm%4r_b2Q!a|Ihh3EE-4;@1)ein#o5*r;G8De}k)YW_z8z)qI
z|I&}X)jclMI?@Nry|Z@_?jBZ6d?roSp2^uu9Q;Z-Y|M$c)L!yvc<_&*TKBS_+xR3z
zFWB<?DSd?<VVPw_?!jOiK?T<3Q?2N?<Xi!1mA3_XqTdcoSu83&=a;h*EN6$H8-=k(
zUy(xIV;;py>o0C*ZSA?`YH#X*{hp=UpYz>TN_)0ZAo<OSilgVvOh#_b<1++5+)3}X
ztzUdeKXEw_l`xNbH~RdBkB#VxN2#CjK7w0))UcNA4{eu2k8NMe30aliHz;sohgXRB
zwP1%#oxH4*d12pTxO&c9H=T(@g=QFG4^lJ=<@UE^TL%)sfjmDtbDj6a95|6tmx1@r
zi*APp#1s8k?aU)cUb&krCE?Ok>SSf3Gu8FviOeXpM*9B8<+@*ygj<A^`uT_s#JW<{
zLo-|k>tW1YwjCGKfS9*t)Hgp*nSFnsAmtW#urV<OZhV8d;<-HMlWhx+{l-}MlU*-$
zbAUGSHSw$4Ola}brCUT4ead3AU~V>v1y?$rCfcumhpSwC*h;0x9*-1q^(+^HnpOAY
za4f+~%^KsrXy*pu`<pDax^FcbV>O7Hnl{%qDumF^qQ90Nkaiczg~|5pUI|1V^WRp<
z<o1_*MY*J@D|^~T0hC{<?OC?b9mXIuC)A25C7wa3euYxX*R90ya7DgkQ!3_}z+IH+
zqkD$3t2g}R!;l0YS23>FXqjQu#gahn<)}lMB_dVTx}AUSeflQ%w`A);0ys##XDQd2
zFDYO1xUd?*S}muRqQxiMAEPrpXx7l8=j)!?<Y)ZV<FmuaO3<iB*`4=;nti8GKxsX`
z?C?V6qWQF;!R(Lc?rgjs@1iZ;eruviMqNxWMpkAIpC<Z>4ycC5A{hB@tE4?!7?d>B
zDC}X^`)-?PD{K%ZY;e`+{3jkvyhWr4n*7u`+ND*d2ZWf&#00;x#!uOiQpU_FibN#i
zB-({H=*?E?<bV{=@?fCJI5fu6j`sI8493sbe=N(}M*4%}+@lo4D!*eT=#8SHUWG~m
z<?2&T4G-XBkRtF<)-uGJhlbjQr6^R5Eu&tFUNoqwuB!0a*T*`p2*RzGdNA)!{wx-L
zm(Q+Af36Daz$#i0(c9Hdulo}YeLWNpEeF@1CFTtnJiqv%^^hu`Ny*48nI}Zxh9Orm
z(GaV$c;9a2!=4qj$qm}C!(B0>5b_&5j6(G%lP6m{zngR%>%-Rn`o`!M_>|3L@IT0m
zG?k`y9r^ZzMoz(4>anyX1|3<NVbFxZtZ(KlAo(K$*XHw#l4M8ny5@dLQhs-q@3J?#
zsrNmVI*xd_)1YjF!#R3z|BS%|x4sb}-S`Qwac}u<`rrddY5dBVRL<#jZf-|gm~VM`
zo~t`sx#?*2>Q5$?Dc8W=34#|l`D66d4xJLyX^Mg*^PRar3Nhw6bnISHOzb(EgoOZI
zhUjMds4v4*3VTBK!&W{UC-u=kBO0(@7%G~}s!|5ZKhz&A%-w1a+dJ7Z77NlyVP!Hn
zkmYKi%Xt~@tNwGO!0qq7$8amSuW0^uBP(ohpG;3)%?9%REwZ{eFHw(;|EgvS`!`9i
zX7%S?F)Cdsecba2Uhls9fR@v=SDnJScxZ_wXS_EK-#{Sm(~;G6#p$(rMWXPQKgG|?
zAbgDby9cl0`M@>xdX5M2y7XZj7zOo_hQ$N|DX~%kW3W0zRExYy{bH^sFJ#%2>~uIO
zC#!W^to<NZc|U=lNe{+!-p2$QvY?aaSA#r53ZNLOcgA@@M2qVaQ?Qci%kCmgC1n!T
zFju=%7JhE;7(=)SH*;oNi8kB78zosiW%A??Rzday+}s6)APdN|WRQ-Egu3!h{0oxd
z(Tybsnq<J%EjlkiKF1R|HSGq;Jy8_L#F8U-QNU?32;FE7h=eo5NYfPNeFW|WI_3*~
zEVu6d@3|y|!#)?tcu_x0Rzq+_x0czx7HFmY7fEvAR3TzB!^+efS>HyeU|)xS==@fs
zN!CdRs1{LiF%XrB@nliTwM}Yv61prleJY9m=?!qd@RqC7j%0M4Bc(4YzMRsjd_)Tk
za1SKR5FXjn?VcsIPtHZK&g*x?Cp^B6te<>{)&zVAgG4)F?~2+6DLI&ie~Kb$4L@EC
zjDz{x_j6NwA|zXkm>y=D83$}my3hkXFFe6cEq>9}8{MrNWaI?WZB4tQR>8=M4q<L6
zc@z6McL&|6I?$l#j~M3g`IHQIm#N*Wv>wwdO+>^*zkLlHVt$%jm@x(U%TNhBEYlgV
zXd%5u4zS9bue8hE@f=J2DOCyDP!?MmE6@UgXzK>gtE1T-pjXuPLw^W?D@PVu|D%-7
zX|7IPWu7e&;xs=2K(yL=1a;A;^l8Tq1kt`O8!f;&jDh^!JQW!_gwB?#X@$L5gnAz2
zJO7o7%8LNSy>MO0#jp7z+=(N+7{}mZkUtRV1v9o``q$rQ|Fl~S+<zSH=XC8cB!5_t
z8-*^T383G#2Kf(wkOebXtyRRK<28(y)a|{`COM2W0=3$=A6VX`APl#MC1p&6%hXI3
z%&bjou{M6V_gY^9_4Vu@L)vh4gewBKf*k`HqLdCep27T)6QbZLDOuToZ!B5C>}FUq
z27mq$+s->B_%oG53CkGQ{Eju^+QU;He9Y>^v~OV*rb9J?gU?AkeX-PbR|Rglgs##H
z@-gO+{3Ss<gDtEWfd+ajc0sR_B-?yryPxd=1zAmmy=}rn;ZIx;nq+8cv=*y6j<%sv
zmdHzGx2<4iZW?6IX`0zdLmg)X!Nx_GInmSEV%x~Zt23uEuUBzyo|t8e!`J0C|4Cy>
zkPoH(bcM+bFh{Cl(pRc@VE;lKb;hL?qe}Q%#1VP2UN-nPX+e?F9yg2%!COe)tIdmt
zkAs?8bX=O;O<@V_vwi)NQ2@hz4{p&25DD@!B<I|Ikm;MUuDBkw=284jmw=BO>H^p}
znu4o~|AtDwYeMgn_aP)gK>zs$F#Os_P#o^tT3zAL!}yJnCb<TWOkH>_?1n3c{umw+
zzajv%<*M;)OVaXkL|D?7xt9p~)j%M`B;%i^vpVyK%-HUXGAzFDor-ZGZ;jS8H`{Nm
zMuvVvdsuI53rB>k@f2oqihDFvygJ6Tv<>Ug(j<RczRQ0^kRx653U<VS-b!~EUs0!#
z?@X~({rb2F4FEiq_olc%P7E7%=edwL@kK3C$rZ){uIc>{bk4}_%b)Fy=A6t2(a{L-
z_r6f=#@uW?FN!rax++3V!CQN4FAr!U^@WYoVwju<jnmX2zk0rqq#@P&w#s2IICk&M
z$<49-TO{9^3U|gMb6WV+dIEhV5fQtohq6aqqO7pWwZl_wk8&0E@;8~%qBU<nNo?J>
z0ojmU)ix&ye?JmUAIut;OhXdtLh{l0-547pTas`>x97(OX%q6cwaqGZ!|t*+Pp|Fe
ztCjP4MMuBQSXLA|ZYPw1GU*eif!eC#r&Jts8Tq`@oN1$?H5<+Qzbw4NhLt0gsOGSD
zU<@{0+d1F?`QMUewvY{cpM3e5oKRI6QXNDlUh{&}5}2-W@*)o{6DV<o10q8%Q&bHQ
z&JxI@w{V2)1XnJ%>%;Y1WQk}biGUNbicDosV0OSY7?i6(S^r;Yf3Yb#<N=spf(61X
z6>^sw>hZqz>+@qrgyRRFj8)ea9#|F;fSm02i;$Hau@~*lxOr&(e8_8T=bs-A`H~1V
zNdA_vhO^h5=fvaJ6}59e3k2sXB>T?utZ0_wg)%Ly0WT+oy-y92*~JkmU@q4k#*YGT
zuzdp4r{U+fgu9oG_OVy?{y4)q(m9ejF7Wvr)bsqoYb*q8OM1WjpDXlu@9^R!k{2#x
z__-rUR-&NIEh3jrkC9<NVfZs4pnjQNiI~MwR4;;q5I2emZy%m&VR{hdRJdCcNe$?I
zz}v_+oKTm*pU6K0$>=GA!n8iUJ^8$!JXMzU`!yHI*Z<Ca2jxL<rQ&}KR{j!ytMIp>
zY_d5$pz;ax#6Js4C=YP+EPBQjR{G&-1|yn{8~FhF8IL@bUN5DpZ~|MH7}3nNkRNDf
z1Q~*ZsNMVc0$RUl8DX8-`Lxg+uwDnJi6b=js;oJ@3*$*-t@c!6*8j0=<7ru%c_*4(
zriMe<Siy1W9#=A}Ej7<SJs0xCT{H&Ac9eB;WG@=-kRB20DDU0L6trCDExTu>tq#ej
zFxGW`3bVS^&wIYpTirTsL<N5|0)!m%Cr9LHbQ{4ip%<sYQ^8Aj$Gu@3#l?>5<)2#Y
z>x)csUBH|NzI<TBUWwW=?vl<)&lNcaB_dl%n4TYF90R6-zHl%41tk@g^&^FYo{kn;
z5<n1vf+Hv6%c}el5n(c!KNdYnuLjXngvUgc5;g5vI8J}um*$=4$86i%+l<4g3nMO6
z%M-lQN!0c$=t?38Czai81S<iNf$-!82plZ6OXH7A<#pqD`bGBjXM4OiD>ka3&LL8U
z#=7T-7~_#%&O-o$;mE4qbx;>JpEnO2HDa%*cL)D`-FjUWK!K*ihk0+K5-y0(v5M&L
zx&iGpVNuLyE!Kau2<m>LXVjm*3!1*PG;p%^&K6)LI!OB$H}jw*<6`ji1yl11C<g-(
z+6W&^3!s@lL%`yiPjmh6j)8O-3FH3(l=a;wLRW%xIWMXwW-p~PlO1ClnN9JAmIp2C
z+&jLdZyN^Q<+B~}uAg^1(IsIRllTu)tv=1o?Y$UWv^Qmy9SMvX@0WE7WJ!-;wh74A
zq5wm4>)eT<2+$2*c0jh~Pxb+g)%7<QCv*_D-QU}ck2`(e0X+Aj<cbAYTmFn{BelF?
z3-fO}-#TnTmvv!Mdj}(Zq!nI&29%jEETR!8Aww{uveOpz;pzFRLm}afuW6Ndx{0j^
z(+4dpAHd5QROt`JxS^x(?XP*&B3l^)UK$dW2^-kGIG*6@RK=_6{rfv8WhzUumYZw$
zY8>IMsG3`RzHiwE<QwQ7axw_aazBPFLF<-)65l5Kycy|7Fa<A#+lU?>4QQlY_u`Cy
z(D_f2c@8Q!(U@R_^_)g;>vqkuGg(x~@S`w5X8Z|BBAmL6LRCz}+JKn8<by0wXlgrU
za>NW5{$I=ip*{sy3Erel$*V|iMN(&pyZlpwFpzUn7phI)sG-h7;2j4mFd$B6dz#iy
zgkL?WAch)JR(Q=6_s=0{0J~o!-wbAj7~yTW@y|>k!Bcr2VRj-zyzcn7vl!=Y=lD?Q
z;sfilCP5v%gD0^=qUv-1GX_9U05x0KH3^Ogvwz#=pl}h?kaZc#mjbx6rSnb;T%3*e
z!Bghef9@sasQ<?97GPC)Vh&mab)W%-+*cX@=HB7wEUO9QoJ4?R!MgxBup0@(KORl#
ztNo>GJix(&=xm9aqP{I`!SswSNg>{=*Z>bx=Y*Hx;oI1Da7~bHJ@RrGlVcv6Ku<`a
zIB3@fFkQm^>xTc1|M4t+csA&WFiWSlHDr`P+-eXC$#(;qjC~F;C6p8@Pp38^M3k75
z7O2sq-<HObi-Svv+DJlvV!9a{a6q!;0Ba^CBO3@-0Mna6y4wNIVDe)tw@>&XR}0qX
z*i-B;)Bk4KtF7f#W(bnK*uY>2L{yHxI;yS%@i`dLioMEt9~2CbFe-fx1iA9qYvOaT
zIVF@U+Rb2ZQ%|S18c8l(g2$?xai?UEe>kyOo%dZdeG)<)pUEt12d|N!ss5vSi%B&x
zPBdc_wY@wkfXa$nwl{W>&VQ>lV=TD2xr_2Z7OH7Lc4I{HUz6A3=*<)h>oxGIxv7*C
z{FFCnFc42vmq0pcHB<bxIH3d^9wip!Re_2-vOioABtZcGzup}U9%ME!Fm?Wm0Cl%)
z50<%vtrO(_!zZ5BDo{Z)F-chI1K!r{-#|zmVVwHE^#ET7Xd_$p`*?)i9%2v5o*+ka
z{%5ZMYDLbAf`YawGZu&-f7pZA2jor{B&r}r{P*Ib-wKh8hk%FH?mQ}}#mA@j03WyB
zr+6!h<K3Qv@RJSFw=4q+^JoeDfCFzv-=C?e-oJl+i!-2mB_$<m8ynmVaqpT0R`m#^
z2VxFT@q!{NLQs>S;NarQ%A%qoGH@TQvJJL>aFDvSwYAKqy=}Np&QnlgWv?amrX!!%
z+BT3f4Gj%_t*aAk5q*RFJvsSg?_C+d#!?Nx{<FkQ2rT(a-fokuNyqtTrN4?uUn)2!
zLs|4qGl*#0PS|mC+U)`5^DXtj)^lugQfWm~tou}**N~pfUMHOadggeM1D7UL(eP3)
zLwc{rAk6#dXdwLRz-{vDb@1ZJ-XeeNjM`15&ZF(Ai6%dx`kA0jb!U6P$of*XqCgN|
z0eTv_ge(Pr?_QDmDxcN<xc#8bvC37(_7f5VrNdhsmuni0n-$HVpQ{HUb3`0-8Y*Wr
z1BwR}3jWw7q+>S0+)9S$3WAJ%?n|$z!g}pP4UItD`Ru@PJ1#Uk?991sFTvCDt@Qgl
zB;$%mT`tt8bi`fm8)=!DOUWAS^E1Ia%U9<+WaRV%<@Gbaw(Dp6BPm%LR<nZ}cG}JZ
zeh68At8<?U*!#gqz{JYBl(iGWKV}oIg#WgmjZ}gf)(bP7JUmF}B3V2>*4Hz_yV--|
z0XbAZ{Z6{DC+k1$MK*gra|~)&Nq1daUzaNgwZWZ@1$HaUd~5RC1g)*Dnf*D=d;9f6
zHh*eA5p_&4AziXz=uYtLV%?O-;(o7KQElxzOtU}HE+FdNA7Ww2t-;6pwUwa>q31_?
z<Mv5jt6Bd0jRT=n@aCPijGN&n-!G2p+m|h7&lvn?0#^kp!*^Pb_q$};Xy9#a%1zD9
zjyszh8=opGJ%0lVsZUByrm?=<F!jOsjqQz8h8^1ZwQe~vUn&9>l;C$2+%L}$OMW|{
zJBM*I?LMpe*tT;I5Edw1USOwtEN1o@D%!7tM$62JP+bWuftQ;$H&J+T?S3O82$(~V
z!J<F}UESTt%*_0X3Mx1mN64Gtoz@Q;?14)yQ#rlM<E_CmD(4deng!75fW?IA=iezU
z!%ml1FVmN`@lYqn*D?Go?CdM6DmXbj8ek8VbGp$12N|=v+{tO7mej;G?PT{=MNQLK
z86vsbPy0MSmz2ma*XD%mYhhas^uzw<aR6{9S9Nsc*GObVhU$8Hie+VG#nn}pmd1^a
znz*>S7MGVV8&?L=8z}C*CftI)F(%=I()1e%h(vgX9b~kuD(%ec*qr^@atEL*bW`PG
z*IWsASQ)OAb3VO#a-f3q0Ud2kd<Uc{7jh!L6VR=2GC9`v0y=0JetD>fyEtH#eGc#M
z*T=Tw!rHD5E5(`;raarTZiel?TJn~=honeSKD*0twHq~%SHEl!ydLO1@qmOOVY8ej
zIdyxwF}1$^a+@D>)bHPRQ090i?5HNkVXVT+(r?7rGI04_$X=Wc@f=;w39*`*S_|%S
z$MO7VduzO!2<{~L3y@mi%x<Jil|wJX)mqY3bh$-+f18P^DI?$~@Dysghw9uWI^63q
zUB;$X0NVG+@lvzC+W%lvR{yaqt@%R}rH);9B3qm5ScOm63{lQZw@!F){A7LY?opDb
z)ywxWKTju{{lzTm+ywl<Ch-w72H>e}tb+D%zR#z7_Ss0T&NM-M@GorrtJqkEkL9#Z
zVoFK);>aj6c0wm(!X0*$iEOon9Se`VfC*LwJoo^mp%HMTr?G`G5VV0>Xc}lwieN96
zp>9GlKn%zGU-Qzpt$&5!jMJg*&5rPM#MB2nwNuNdCNSX&e9*}>)`t5LYOGw$ShQ?^
z>UAJrMf{+9w?7JWdtxEnZnJHnN1JosUQPQB>48`k19s#2nR|cKd@@-OuW5!i!zbRQ
z4H$P2zKl*D1kyWmRX;ze;G-?M^*X%mqGCY7$IY|A$*qC}3(#vsO{*6(#x2`C*eeJB
z*?FwO<kZxFt6IIAll|u8L-}+At5RLaGcIu4-i*(HlH$egci{e610gG5UXPJqrULZi
zo`^nJUG{_L7y{v*a4$t0SyEWj$AV~K&Uf0?q316?KIi(i6SAsMa~!&#nzJ;Nb0w*A
z^6jSfaFdP>@PxB@6}|Ner2?Xvs|3m|-ic9NxH?c!EN>OpE7$T=HgE6Liv-~ws6|7W
z0f~aJS88)nI&E2Y@LV-~^eML^bZZRI2XdKrbGq-K0>fitU<rxG8!*dzf)2FZ`vT}s
zW7MCbGi&f}Uz4X}#MGu-i?vskejM*2o=K#prWUn|FvRU0<lsUYGkklXB+~y;E~&BY
z!QYUwC?I!oAGNr{4eiH!xyjG<5Ih8c0$eZ_K-YDl4($LAASr-K0HKbFnfVEg!YtI=
zKQb~Bu;eY?14(N>Zu&gHSZ8Lefy_k*^ETGh*jU}v)U-Im>ppN&sDxcL6A&@P{J{CT
zc)Ae56aZ%beJvlJP=rZeW0g-Tsw3zY^Y;1Gr@gbNy~9y<p<MCz>i+4`-aUVnb|FHs
z8hRGnRiF}TJWqty+Y6Nc#m|5H^>48ybEu^YKCMrV#kU~vSA~%eIvBV^)t~c*Kj&mZ
z?MXtd;m@7_dTFf#y@O~Y9(;7izwG9d_CFAR+oZ(!3dtr}Q-pR)LT(GrjM~9eq-s{<
zW;1j!{XS1&;qEXZ*wMLMy~sGf(23g~IP?#?Ld2?fpx^oI*bBTV6qXo_-qQ2Ip2ykY
zvcWYnZwp6OyU<O2Tlc}iUFdA-n&!Cr%IE>F<g4<}1Jz5@RyGk&hp+8&R}n-L8GuaR
zH(4>}+K?gY0_!iy=CT(CjzKwsehLHC29<;rBw^u&$^@Q-VZ=<&iFTcer^2%@&t-R$
zN!WuO_B^Es%EYcHHMB8NHEWB^bCU}Lhp09?8<Wn}22GsF@T%E{n6WK?3t_-F%yDxt
zX#+<?Yp44Y!FnQG-pK(DG-J|Kn5p28;}uujSs6N9e=%2Tz02~k2R$mRqc$+)eC97-
zPXuh7#M}glJvy-vb;Zu?VB3YR6bZIS<juKq7P&7KvSKe-b@T{cJm7!+tbeCf_=?O_
zfb}E>xec5<w!Yl^_-bK5jG$C|=xf}DZ|D=PoK3R0?~dV0j^p(U=Z^nLk;_m6Ue-9<
zh_FO_L-y3e>(~_`L&zasW7^j`bYqcG$VG5Ndy8&&7M_55@sZ;siPybP-45o5s)qwD
zr<3V(laAE}Rh-Gvs+k~p72O#eE7$H%?^)`EoiV*o28Bd*RCQJ(=RYhQBmUIxev-4w
z0YtQc?VeWJ!E){5p4$Z!+X1{tkSiRYPhU>`$S_5(Hax<aEUcPo1N6dfxJlO>!WhV~
zpf;NYX)9`QsZ`AwG6yk6)Lt}LsUo7hWq{auR~W%KvVQhAbS{V9u^I!4@4Nk}gy|sh
zxFxG&^;*Og<|*tt*y5%eDZ2&sC`$+op0l>CY{nJI`Ikoz?D<|=IOo{+ybEH`k_&yD
zw*ED#gW_YmuwSs~gK+6bX%l~mcmL|<z)j{HQ3og@Rh{Cgpv22bt*0lvI`d}VR8*{T
z?xLTZKLF54KK^*)D8~`kX5xlRz4ZcU3@cuV#a#wd1=s`dc^}#m(Xl(1ippLX%P{%%
zKR_w(<N{s`)xwuh|F8E!@!gSsYk<c!Ip7*K#uH{-DY6mij`W$DStz-&dJq&2UQLGr
zX2A<qn4;fOHz6iT`^g<E6oVt6&M|+#spy_p#r!=ZuF^vNX~E&Eq6uti-<D8Ou!Mhj
zPP3=~@%F9vJiiEbTH!}AMy$Zrg)5QW9fTvioN-s)lpyC5lkF{0+mkYzIjAgbl=On>
z#2;(@+toidQwUh>oHk)Hw~U&7+`u{C%NT(Eeh|42YqOTw$u)(VrPX~$GYc3!GqAQj
zcY(K)T@^KluRpNX$){D<!GMzQ9qk}+Ll_MD=!+`J3KqAfD`p47px!hWp|^@qEO&v8
zBD>=n0+uFiR8A5#N<W&f5}I+}GrQIAT1s7*`{UN+j&+}rJc7pvqtMpZr%5gCVK$%=
zGuh|tqzv-XM>PK7A5RYI<OVLecDqr`bIk`iOV~=`Hf$;4xamCDV5k#E-U-JoyPe(n
zLJM?#8IDX*7LE^oqD=^r2Eqp?jBGP4-7g7GXdm?vA_An*TlOeFR4GQuU%8U&3+)<H
z+E2wFi>7;W^tZ5K6raI0?+*fcfT{>HbSp8`VLJ4$;^Zh%NM?s$4Li~Yy}i?)XT?pg
sHwM&ZfcwOErT$Mx{m*It!QFh{y39r0vZw~U(+$#6*N0R*uzmTz06UGa5C8xG

diff --git a/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016Trees.png b/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016Trees.png
index d1b2cb97202d660092fbea57796b9f624fdfbaf7..485d0e183367a5f0c35ac633a7e25c7052cb98b6 100644
GIT binary patch
literal 18884
zcmdtKcQ}^+8$W#A_TDp_hU`(c>>_1kW^YOvWfM0c*_5KJBxIKv*|U;SMD{2Y*|K@g
zOW)u3H=gJD`#FxM<MTO`>%On+{XXC4K3}ivp^nxia*`7y2!fDnsH<K>5FAAW!9)`g
zz;DEi+3etlw1=9phn}m0hqv_|d*rIMhubYzk6SlvIKAxe+`ZxIA}(}JNL+x^$-~3#
zu8gqo?f-s2$n}n+a96r&58Q;<P5t^^1R<qG|HmNDQkf7$LsUamN#7^+&zN@ro&7Yy
zilc*<B%3;CXfGGt4XziGT)dpd#1UB$<a<4LB=mLU3LCFWHMRSuN9Ij(aT?NLq%ZR-
zbJ7J<ah7i={&w)5J2(*iPM&}_r0C!!-m|qnrSeu*c71m2^U|ZL%gRI;Zumo+5F><8
zQ&T65GhrhlB04?(fAmA(A|p1h*tE)p&EfO2usZ*(EbrxUD&@@bayp(%v8`#|<HQ96
ze!6oLZ|zCP{g>qf_E$UR<Xm>vehyW;^H|*59EhqJw~S$xrb$RhK(O}-DV2$WlarYc
zMMFb|z};WDntpW+CN&;sFKkcZvda6>t*)-R$)t65celrLsfgHrr0aYew4dfZ<~eA+
zH2ThIAU~;3jY|w)gNQ3y2v67i>Ep+tBd!f>wO)Th+H908EZEciexK!y455S@W?je*
zqv911P&GCll8(Fk&Wu-3P)%K3J*LDU=^^bIo5TGb1nZb_k3lP!*7f~r`}M7X9M}3C
zf-=XR_T`2n3PfF7yJNt}GUnIUT-&Z>(TczW&8dxFU-OJg()-#t#mJAgzeeQ65>rxk
zL^F%W!%|!u4*q6!pVIi^+d_%(M&9`VKjc<hY0uY`NK`oW;rtn?x^($+B-daGaX1I}
zy*H%Motz*TrgKhIAdEB7?D8kD-_q9G7%dwe%DDLWNWSK3UvhHdMT+=ucc)61niT06
z&{vIlvF!f+6Gh1GgM9e#VSBwt0fUe-3bjtRMN!kyg*G-SUP+f@hD|*>qkU0H2@|;Y
zM-ox=@{-8W&WIfyHIrhc7`^jJQ2ol4*43Gg)vAUAa=1^js4c-0LDO*BGg_0IgO-=#
z*yX!-rKe|R3J#cOW@g^FO(_R#Pd%6)uai|)RxZ6er`2CzM7ciK(=lA(Eb6^vW?W<#
z8Xj)5yYVX`G7<;L&dx^f^Wdb04jum2oU7{|Mdn0LKZTvX`pl<ZVQ|N5Y4jnZWdOCr
zb8<FWuh#l|d+|@7KFG}oJmEZ8+~z%T@4<*mEjb|ttK#L$B<-<mQJ2_#@i^{nP#YE+
zW1`wZw9;gzQXJERKRc-0yvYY!qd-7&s#n`tL>;=A>>M3uzB^`wjk!Gd`m43~g^K&c
zy)EnZXy)zZprfd>dO1EOg`*|-1p|>QD{lJ-%R!f3UW^feT?Fj>d|LfGzd%Pv2P^OY
zH&gSeP_p#hS^PnAY7CXC{(^+dD6fpiJSQu~rB@@C_cjegZ}#(Yg;JST-lD#@yFo22
zEv-z%<<OPPm2)M9JCtg=Gl7Tu!Uc=$SFeb<Le;gj7~uS#Qz=JmY<Tp&P$7h`hJ}S?
zX=hw$cBvxe3f=zkNE<y#d3z=qkG#2I?@{*$K67c_p;XF0e*B0C#wCpW{9MvVW^QXD
zNH~<rX8gVH^iu8eh2@@|F7cj-GZ_JEg(DNbb8MnkjYz_yM?7XiPm|9(KaslgBO)Ry
ziuU#E*RZ||NBei(JJ`I5xI(4w?Uv7vRFxPv1lGZ_3<?Y%QuADdlQQ_}$tY|gAk?sb
zr)R6ydzm3lZY_eBmse_eXVI&cNzSL#xIW<B+?zBR&$WKo(WuwR?djs$B^s?<z3iNv
zNVO=2hxVVIJWSxZ6gfR@^CCarXQS^@TkUc^KZvF8631r0xr0Ma^0^y{@Z1z}$4E+r
zeH|h$RbAZ#CWXL&SI*TX?|j!C`bsPndYq@myeH_ZCIbCm`PEN_vxJVhFur>A3YFV*
zoWh}0Pnn9UqYWcdLa@HUW<Z_Y2O2Z?HhOT!NG5rtd3}I%DAhKIskg$}sN7}j1VY8a
z64h|<$JH@STbB+oSy0l@AU=I18S7k9_qT9HOiZlQx`iN`O_l+nqNWZG4aF0%cwha+
z9UK?|;;}eH4dzjomvLT5hypx+ygjqF!a@0*<IRyuL1c2+xuh+eoFzUb<snFuOG=9A
zU~h{MIeGFVr>rasnA$+Pf}|uP7+NbB$nws+u_DJgnTfxDM&P{e>%P2*L1a9C>)UI7
z1s|BnOEO$}JIZ~g-TFh~sp{Eh&Qx@Ccp8are<}uTzQ46cu)CWJGhjB&eJ?LjGc!K~
zWkrw^=bW$=&Vx2bhX+Iy6vbYXyI)>uCd9{2`U;|(Vv%$~O<%ra<Ie~q7$63N0oOX9
zrl6qe&>|$ljSCh@CTLoThos8+(tye&OSy9+MGM)mxXw?XT0T5FBhL};0`?)d5D*+3
zyuH8PvpV&F!{+becW|(cdfM`y-)yzh<%p3am$--sDd(<eSw%cNyv8Xvv;LmlLHEl+
z2fmoh>tzm|A3ij}?c9I&zrMVsrmD)RpwKY9eo0l;3fwbzJ8k(*It#yFr$Bn(6VuiM
z+S=Ms2Yf~j$3*8{{F<<3J*$R>27=U%EirdpfQW&{pz4`e+1_e*Te;o&m-7=13gC1?
zGc!5m4!38(8&S%wV9)p*Y>vV<Hv%}=*_Ync-T0b=@TId%evw0EfT-HVcXeGUG_C3u
zH#{jLb3)LhyfI3+eqQ=CoE#i^L~QIu6BCnH9l6=rnc%RJT<%J-It`U_jr+`y)6vmQ
zg7azXN)hil!7<Iw^62^Vg&oDupFeL#H}t7^c$};8`V;B9KI`iu>-n2bSw+QtvJrFc
z#+U3bWN(|Bm4|LsuBRJ4v6ox#60ctS$iLb`sbIA+-v<vZ{u?k-<=O!eY05*!`i+z8
z!#NTY$0tFJSWNX?y1!i2&)Hd=UI!2Pjb0U>l_tVBx4w(XZBO0@>y{@WB^7m_R=tuc
zMZGXs(s8gg5hr9`<D<NiGUt^+ima`zA(>y2PjlBuE#mNqDFy~!NcUfohJZ&XIqHUs
zaEgjjJAW%|daaj>I641D?}}iZi{BBPJ$qIvU|R&STKHDvb9AuPBjbAUB7ud^6h2}S
zrIxFeHhJe=w~P>kGh9N7R>_F~T*W=Cd7WQHuI3fWL9@H$iH{z+FAj+!idtF}r?pav
z3=Bj)=5>Dj{At}0%Z6aVd5l5~gGl~9^X?@zHER&X_F`ENGMPFT6&0n@5>Adv2th#m
z?yEeYNZUH`@ZrPlU%44pRU;S#lzk&~5QIbV{rmU$lf;n4gn#`NdQ!t@p{T0Vv4<To
zk<1ym8J1^rDzUXyb^ma0Ii0E;JjlkvU=OX~!})1<HqI;)!vW=pQ>RWbN#8lSRI^Zg
znUqV&w33XJK|s;UinGv=KRxKsxBSLe0%WD(=%7d3mQO^4O1NQP?3ToHM+W1J3-?yQ
zK&F$7ER;-4PS{<)sHlio`2M5^U9npG@j=RGg)^LjEz*13mk>fo@`ZacckbNb`;7Cc
zr>EoI*4Tp|KkQJC<}($>xH5U4a(c4qKGXS|bdZ)A{Opl-{w5#*t5GIGQwRwA1et=3
z+kZz~TU@GaCmVy4*n?(!PHNDk`%DwJwzXk;O{yKbQ;E6oLa9)#5%HMsEi2v|45OOs
zO1V!!eqwTEss+7!jrVdav$#Vh{25;VGxP>G=xA?(nu-eRGFD?fSZt0)+l1ug2(V-8
zzAQDT{yb`z`W=T+&q4Ea=OD1d?bQwrG$-lp)p@_a_6aqRCj!?)!blmL9m-ymmQrnv
z-lZ(A-@RFAQFr2Kcd!B69jc~&8&2qvr(d^<IdQ(Ztmi@$xL^nZC1s6=>lsIrFXJvK
zg8)(e+~1kYK!av)o-WV(?dhm=cP&*97cxavJHMTsouu{yv%8<qC(HSYg_AMcn7xR|
zGlaA&;>t3p$6pW0SJ~N|{QRV2wce7|`l|ZRrHGzITyX`Fp>m;OWsQFOR%{=hSa|0&
z6ZHTa7zSkgU#%ILzz3vI;6iYKA00^AB|}4{7<NW>_UQNTr!~!<)kJOnl>e6EZEQJi
zeb2X~xc|DH-OvtYKJE~XzNP@f&wauK!i7)%h2qPl_3vAD*Vq@j8j_3g=O1ZbPy*u#
z2?^0LGz^DmPLp_&*<1h~`X%8tPVdrK?Hka{p4iV2jEMpdHjT(QE{skl!ZH%luMFUO
zZLe6XUb^I8?>hcoTH@A_mDNMEBq7QnQ5>uBL=&Y&FWFEkbq$Solb`9~THcb#m~sS*
z#I45m7#3=F_E<<uAd8!Zb1V7lll!$T__nI5s%P};b_Amcsv#FV2ZC!asv7s}o^h6W
zB`PLX*seGKIpY7=Zp!tXrsqOP9eA(#fdWRbkK*cSlGfH%Ci9c%W>S9P^oHP*MhoFk
zQ~$EsTB3WuH7*PJ+x%FqFf0+Vnr^M7VlceN-;t$eIhpgkdrVgGut)!X=EI0iIT`Pz
z$nEX)`Qh^nk4}%pJzz)c2Km(?Tgt{?9)^3>YEkw5ZywvxeXW4HETP_|?mSV$q2CP!
zmvHjC(^0~g-v^o<9pbKL9PN{8Xt0!e{0i!;^OuWdm5!cjxdatXz45y>Mmh$D$%&&w
zsv?t$b}Z)UOdDf{?1I!<5Sii+J$?G2D(#CSm3*N{rK?eIaa!cPJ-ht{OT&#n3(7O?
zI2l1MYDb4c(JXU~v1}y6<+{4Yyw<xLh{y6$^Lo#S`+R(K9&d1`y53cflUeIdUrwIr
zh~sFvDmfN8Ubi>B@k`HV`r)PCT5n4;-=A*pj}B#%&;1HS6mQ(Pk<^iJWQ%aO*gIU_
zUKG!~%7VW>`+a4-hvD3<Jmo~};w|R40-5>4UZ*DRId00i{4pVQ_)H?T5MWxxm#i>B
z@<1iz^J%xw=e34zDGnA3MKjN|3ODdZMAB5RU4H7Kz>|<Nko_u0B2CU0hv$;T{o5mz
zHiN}!I&`FK>+AV)hD7d4@O*28TjlX%WMPosX;|OtOSU4b6oND%h#Gb0hraOKikq*d
zx7`IhM}2c~N2)$`_B<8wDx_7|w`v-%6Z8A)VLd2>pR0x08eeG4Hz4`@M~{mB6}Pxv
zbK)rzCzI;lNJfi1-j<;^6sngB@{A}WVmz&9F5<s<(KzP))Ot<}Urnts$*9I{*3g}T
zaMA-uNonBdRz3+GBkqeANv?ZKvZUXN4MglBNO>;FSWmffBrHkX9*zJjW|e;yySOND
zC1s79m4Xl|oki)3Pwa`f+$I|XZ;ez|49|2ntO_Q3<mSVZ2(5p9jC^WY>xKRLnwwi0
z9PVWu{gX-t!NukVbo7|FWO#%|7kX^tl9Y%P6R~f_CO2*n$DAORN;4_A=26}lj4Kv+
zWF9PlYZ+jzcIRyfyS&*@g=YJ7o1y;CqN-cGLf@`wo28O!<$4_k<C<?D`jg(dQ;s}&
z#51++PJExR)(sS-<-sX3*7@e)3KOJu|IQgZ=Ap`~!JqKy=x@yJZ|F7Nq>qSYsQ%4&
zhK44Uh!_KM<(gb15aiB(QRnZ?Bkjuqo4l~|gJ<ZEmsFb8`}OWUG;#Yqz`wdwd!$<D
zx2a=n95GN}#LYEznT$z<=<ZxM0b&+(&!(ayAxM1ea_@NluPj4Pd!<6nCbnyD%TDd@
z7KA*eD=OfZGFBwY^mL{#FyfET9xf8*Du0p^Pi57^&K$*XUAH8?vF{~~*HV8--xEO+
zuSFd`&n9!e&(B_~U-6ji;3!hRD!=!>uaD2AZXmewwgDO2Z{?R4=MD#cICsew3Zvf8
z$mn;M04bLkl17Nz+vfZ06w8IZuJGDrp_VK)LskWg)h30Yz&E*3uQg~V@`Q8sSXBCp
zo|6hOu6)&IP`yN2ZPqwkLE`sUzwgC;Qp<bxY98f^wO)MR3j1o-WIS}<col_wD>5MC
zSZbzJFs7p76!FL-m3Xdruxp~pfINP3lWTFIq(bCzy3kZ(aHOEoEs0>4G5sGuHRdNK
z9=@?nBcfoK@7LJ$XjnZz%l7Rl>fj6bJ=+mggbCYizvaUGfZ_ww`H$Ccq;_!xbsE<V
zhfG=p-}s8dFQ4OV#As5f@4oX>=*qJnE2A#qyE2Rh_=bf#@sI7Amd3;kOAI+fJ-)fe
zuoN(fw?EX&J-M;q*cQ(f*7-T9x2D46Mi+J3=fx6H*Eh#PipE4BA_%7Og$m8(%O^d4
zXJ5T4%=e|e;z}}A=$C86UvrY0U7nWOM}-6)nE9+<Wx1@$f8qUdO#zvP28oQf;p*)3
zR|XWE#hRO*XR~uPrS7O+bSrHH5#O39<`mX$Zn|&a{o%Y5r`uebeW^)>P#c$unUwD}
zW&OvB`gB2UnELkjZM$n0=C8#GIB^;gkG1U3U|ba)eZ2SkZ`nZZZQW@feK`h8GH3}Y
zI+-qvhJ7#7<gcJIcvakQbejfe`zIs*Nafjx2rA=uVo*I3or6dHP#<XOw4sJb!Fc&H
z_ViV}(^?*!B08D6Ji)V&JQwr~CjT!2@1n<EzKlF4*Xiz*+!|+zu>Zu@^Ca;kMgzyC
z_A_|bV95zGMZ4@s*<}x_HWJcsO7H1kxu3dO{JMX{ZQ7snP9w$?n!K_a;Je+`mvfaQ
z@e%zw=NxXqtKpvvHfd4q`Tm_oSsCj-!%9Sk7ZQSZG1jB`S3i!?mFSh<z8m%dF}@Er
zf7aDpb0ky5Ydpfyx&Dl;`%1?S<$lyx{KkpHoS4C#y(q8H0b1jl{*bn)+eAcEOkzV1
z=^KpuKw<TAhO8@af-zX6)cI-j6G4Oi+K7o1f~ls?9{%DT;w$sYzXf<`hu-Na9qi)B
zdh@9d=QT2Lx=n|gy?*{y26V(ts$BH)ePM5|!gK`Btu0CG+`}4ug@+dccF#K`P|>lF
zefTYVqpUa}dEs?&KQk^BW~Btte|9appa1W?-&O9KbIr?<s$WwCV}!ylneT`U>RF5v
zLjvUy&FV`IDOt^lw3)87d85K@YS+5+q)^{}Tv&gclr)ub6o4DUa*6BA8J0J1+?ga6
z^GW>*uj5cLDi^9O-ta=OG%p$%XnoROo(K6wMxL~DxEzm!h7;4#smGtp47_*yO?_|g
za8IXU0nUT_rg}aqaWecuPcnINgr0_`vVJI=w{wXNU9aP7ZpKqqE-x-G6$#Gd{Ik%g
zXj?N7uz5a*p~h-Lk7UncVpu5T{UEeZJeJvmbmxrA=QXvF6QVYBv!DG-F3w%A*=Q&=
zxeb+|2HF?eneB^+T6lOd=7hQa&TkrmOc_<W0tDO3zwmOW_I9`bezCb&G24mhox>Lo
zKCU#AdM%EgeR<EHo{MYFXXR9TgG8O9T^c>Z&5;m0$4|!fqap9*h?$vb3=2y$v++ue
zkxRjN)lpA%SBJvM4Ls&EvoaNxC+daBm@>l>UB=9u#%lgHSC-VbU-qWw@tkL`UQ4^k
z`05*Ly{7_6ko-N39%h+Bc9VO3hp8O_M+a5gD=prlf%|ybIZKxU56_z2+w>eNzkwUe
zezl|Ux~pwlWJdgxCy@~m;ZnY9c+l~bhVIhzXGuqHUS1--+(WiUXB1)=N;qQrUb+^p
z$*j)>g+N)vEm28I!FCzSPd%iu>Hcfu(n-D6*7BCAmIrQof4o(61-9oDjxY~eAjsJ!
zqz5t;nRY-LCJQB`WO~|st;etMzys*v*YEzm3rV*H9X<W@=HNtV7O!!QhiKr@z0K?P
zzrMWUKYg`^<JYfWPQ&HQNTbagPB}SHUzd*F7cD|r-xe59$qV-3#It%>7cdH&<&nFv
z%k)2NRV8eXc30NLR!dcU`>5`D-~aQA6*qcqIo;WJb%pzywQQ)&3k8^k7)u>u;4j2p
zwV@jx%q$X4C4G<S@Q)6?+k9Eh^)jR__A0486&kjAJXTu(j`$$~xoX-dpDOX-c)0ny
z`azkW%f;_z{r0zKyyttT#76xV{`f4tJEME;TIgK*^YHIw6OFT<H6o&KMYFgt;a-0t
z>i<`v^v2hVXpAgrI3ze+K3ZynFbc<Wn@QBhE@==-Hze?I$3Nh3XD}j~Oy$nngxuEW
z)EyG`z-`z0Z$+|)+qR^$-Dxp{mO;2rEz_}rrV>-#vdb6*I%ZO2tWrdhqap<Bb4MLz
zv~?qm!S~TyC(G@)4mAXE2-OzSUW{Xp>U-H?xL4NE?$z<^yvg8o=<0s?O8GJ`Z=Tz^
z+{GHnR~f~q*I<z!9yn2q&e}&mK6q^`e&|(U>~SdAKKf?LZ*nf(M|sbZXtGDa-p)?F
z^gA{qsB&^ri5QG7`y~F(j>*K(w<3~Cmw_A}A!Fo9dZB#1w<{xiU^N*ofs}4Ty^Dfj
z(HdT++?i0S_}?GPYsFJbUgcDtcmBqTP@Omto^!RLq@}r8$IL9cJ?7;K%mOk&S=2Q(
zLGhkqkmeR1h$CCm&~s?YQu~xI?`>l8Y;GTsD-uHDpVe?Ym$=;u{VyFuhf8j5I!PVp
zZx6FVw(B%fX`VFbH2xkRL2n0nlX6$$sUwhoLz?HUr$Xk?)upr^DlO2_*AMNvu*cNY
z)HL7!nlvlxS!!+8m2gZR1vTRk&c<g+hLfkxc79CkD^4So_Y>KVzi^VcdQpu2>5U9g
z=nrKLwEV(bTPt_J2nnlHfU%I}UT28Hp^lC*(XH{q$ynKP$^^*as#h-(np9k)W+~YH
z6B2hPp6skn2roTV8((((dP$ku({5<R-`BpdLv!`nxz$ioMHbcf{##mR-eXQ(X?qgv
zK>>G?;#|jS$ia@c0)qCpMYXGM3>M>tlfA?FGhA`$`gLZoB7;{mqp@t7m!3-um*Q)D
z8E@EHo_O&0kL=?KFE3uLEAOcvK7_!GQB-QWv9TaY1&<1b4mV65(2{7bh*BKY2BB=y
zTb~%1DdluljXJMNN-uQD2>+N6VBi$HMk?A8{tj`PnosvHw`U_E4c1num>zL08mcpG
z=Lq86o}m;J;EiltnbPw3m6fH<!AL`67Pwhs*evLl?$($x7ZQV+PyGJgmq$!YYx)7l
z6WiWQF7>NpaT!5}n3xl4+>m-wu*+esTP{u(RXKpuk-c`!YqB#z1v-r6(05h<Hyq3^
z?>BXHxO@R7HPF_dE)=1m{Yt@eF}gWQSW9WHo6UW9<7}bnmaO03#Tb~-V4%@1{p`GR
zaBAw}<r!B<ZD)IqC@ENn;-FRSYZ4^m^`49)K!j0ANl9H>Ap}}z0b65Y<*pNbdWS<p
zQpN&vhuhsBKQ^aIyK)H?6Glcwt^Rn_lfEVD&~jsCZc6*>r1tyx*ilMx>dk9+&-rEk
zRx@D_l0!%thD4m=Y47b;uXOU**HCyB$Cgm&Y}z{%mrdJD?kx)%m$iq`o;g`)S)y;U
z@<xS_(Z<%GwY?@jd0tij@yf4VnfOwczhkGIYTwd^)Znw2vm&#B_XhgIzI~@u5a@VF
z+tG7kVTNCdJbgdbpJB+-6br{;>O!oXtHt+3ppW1}&ohHauRqE6J}BT5;?A^7i@Kyr
zbkDaE6E_^>#GElH?}WOOZgC`cvf-%0X80VZlyRo*C96>>{%7ZDoxZ55nWg>~FH*f+
z{q6V6$AVh%CiO=N?lcSE4q`(>a9HJhGKbw2^!J#szvF+`GzK1i56M1BnwVI=y!Y2r
zY+omhhD}h!I^>&ygzwwRouvdGF-TQQ@4UiqYon>WjazxUz?h@JUX%XzaGuksaVB_+
ztSr0}=f8v`448~1-F)yfI=J^otzpqF{i#z)gU*-Lt7)2=7{_li(huO3)bGY=nxD(Z
zKs3m%K{ggjhQA7E;3%snOZDv)iAC(IDIy|1r4r%ul_RV=21TE4ea#UGSPjDkum8i2
z`BH3W`c=Dz!y4|@&~TD-xAU`fd2aK3A2K%bHO{ZYXXSSYap!w4Jaa<ElN48njXOV?
zVEN7s7JHn=>9$jlO+)j1pZ`&p%99GuD3^NX>gmX9$nbJoESuQc+R5x!H*oVwNwEF;
z%zIn>Kh2#QcQ@%>6Q-uX@7DR@9PHKhzAKA_^m5yW@x2q0m)Oj}$vq~D3~(7#Ah#!k
zcqENR3OyzjkDf8B=$q2vN5pvxA5AuAYtR>ErKHfn6BZ|gXR0RXvR{1|YOuWMkQ4o0
z?oZsq?Ci)Yt>;%W_0G@>n&!{<wS>j88T6M3_ZB^mY$P2?Ah?Vhi{bvl0voypy-0<F
zZ27j9aQ7P7zA#k&?zX}3d2Q#{RE?`MiH@J*Dx3wS{Pde&sEBzjJx)k1<dnYNwDyzU
z{6<rg$%0N41CG19iP19&WN)$f+^G<AD^)*R@Eoc))JWs08u?3xL>yx)y&DaTR)saQ
zo+X~lye=N>@ENbh3%~gNTkgCcvp9s5AEn$B+h;$M#IikdpAHR`_opi~&8=$-+%!-V
zx!JF7S}iJejU_<dBae3Tw{wT|cO2Dhmaq7kaTj)pptGyu<0D1EaUt5s@*aIK<FMAB
znSSw{rl6xb{JXPwin@5|zBy#X#QUET^CVPNp_9Ysw|Px8FhC6}?R%{`U0#fW{qY+p
zZ*U3ipnmD>ezH2^YQkUSuTXy>n4$~M%_lc9iNmz-RaE4*&r?TdYVMkiEA-g6Z{Lcz
zj?3K|t)_k}Fw;cH(Hcq5?{=^`$YNfu*=)d$HN0JFb9n7f%w`zlqR%{6_b&Gr(?_Rq
zu0E?4@o;Km;^BfR_9<2=&Y2-mW+;J&-d|U`82iK{QcG%ev22*bjv-31B^)PB)_G<N
z8b9ve?fHf)*3&klBKA{W<G@sr3VLDC;?ym%kf&p0gcQ?O@>lI+)-XOgB<?E3MT-=i
z=U1)vePBLp$)%xqB^v&dxO%iyTQSk|s5Y%r1IY?utv>&T%(T);yiZK*q@7)-aYg=g
zQH_OsaC;1zd)jnTF`85v_SM%#&VGJZ$(SH{rHZ~C20(8DcA(0;2qSoyc5wY*^;eQg
z6EYU4uaMMsI1MgtnBu$qr>#m_WvisO1aFaWYZ0?}_fLY2jFwEMhTJj=6<6ApF%7sR
zBCVo$i6@&}2(?m?Z$-F?iCC2fkkyor<bTP+@693aeZwQ-MAci$)mz9+#Zn-Muc(@o
z!iW6%UPcJL>Gs4^npSU&MQq#2I^RZrcvLX{lX2y9UE^%mo2j2qaltkz0`}ij7C$t}
zvC&???WWw!`^icwjpm6Ue{uOprSMF>%D8QsZ^z(*v-b1bI#K#&l?}3wcMT-FNilYg
znh@nneak|wKBr5S;<E0bz|Yk-8WWziXyE0$oC)D9>tzK)_O=iG)D+I!XujJ1fV%ns
zU8rK0@C^weHOf@FPms>$&cnmwo>ile8!!}%JDhR%>{)t2y(Kj&O)lHMESgTS{nPtY
z2@>=cf_y4{;mXD@3nrp?%J(gwOHJlT{gD{JXO%zA!>`F=S?*mzR%_mzz+=MrjGt1f
zp3t~vcX1t`;N}98n%Zk3(yx_7uPrS(^F^V1O$}MihQflh1`Uo3fii_v8U28e9)9L0
z;RaT8>;e!2Dh7sd7$w?%e0=sI<0t1O!~^<7Uvq=~HnAbM1d;DAGJ`CiF8MQ4_qFLl
zxXymcb?~?UL<SG5;__M$V<BB*W&ZL<p`+ZY-l@&Ln;1#rSHt%CNH#BtMZl`s%P4o<
zP~`7&$@V9wEf@TQ-FNYojHMWzBC!_A1df~Ainc$xKH>GRbNHW?W*xS;p_Pl5OItcp
zQKW1pM11Phuzz=0sWA*AD^i@#n_pLz!e6Y_)Tyb<b2!7f76O`Y9{-4kOQ9jFC2&rQ
z8Sd`pB;1QhG73J;ebhJE9`593tMW2x3MQE0(a|_ktcothp?^l`p?Cy;UFw=qIb^F9
z8@hPwkHpktEGr_d_*{aIhL#PwN?9roNHZ#h&2Va;A7LDy(B4cT`QE-Io-a?P7}!2`
z@U!x6E>2@G&t3&$M?S?uKy7{k+IM(JUms3i-$2Q=KOQ*#TOYT#ZTahu#v{wmF}v2$
zW2?qV6n&~2I}@~^MXK17E)*JW7$mQeVD<2Av+pQoPJ{54AnwhnT<$L`U%&KJ<LVR`
zn#k__{UcU?@GayIOT`}U@4I2=xW7DY!}0h7##yyER98zi5ug|S+|`Sd@3NXjdZ=(i
zxy@*`RcmXg&EgQJfQg-wUyZ)~7UcJ)bYI+4os)I{*pS9&xW^s6n<5|t>#$z7z~36B
z4GrWaKtk2TzS1PP)8R9n>kL!6Z$_oO_wsfhH|e*+H&%l}%+7-*rCvpU`)FxjPUjb-
z&i6KjK^Mqr#ISL=KQgfhyY7?Hq(D622_iau-hfmJYNh$F9bpjv{WjdDzDamliBh#f
zSX-k`#4C3#vH9IEb|uXud_TZy)rQ}&xZLU!1ODsRV&ncLHZ5gB=4RDZ0z_j8aijMr
z=H+oSy23D>58fU5MLyJ(=YLmYiXE=dTmAX;6tseS-@j8lG_sefOomo|r}ud$1bShO
z*%}m5X%J>fByROeM4G?*5@KFUSEwRVgPVNxq|qoNj8jOftua`uwKZ>kILS1y_f-q>
zqvgVGXXI-=+|@2|FS#8%=Cw5}oE`>)b9*G8hzL<usgT62$hJA-!_95!_y&QtDr6Lu
zR?Zs4@emAcUyunP&v?C{n%~|uVs1DzS=Ak=uOnrX$%Fo0Tc*<RAUO+(3zepYEwW~P
zwEaK=*Jid0K_3JuQc}juj5*QvryoQWk1-Ju`$jrcy(Jl>ZEvH?@f(xgv@z|JDBl{*
zL&XIBz@fKlO@{}=4a*IL<31|>w_aZ4^V{-zVXqksoyd*lWo773-m1K7`KT)f1lQJ@
zH&jz$QyUe-QjuGbFDNu>YOUS`1Fato0#_^gr{$&;uV@B85lpN8b^10*;C1HsL~j4)
zUg%(b`C^A|XSL(w^1Z~9y;)(3%i}`jw+ph=Na^eR-a!u$8<r7(N5GlEK>rG-7e_}c
z_Qws(?~$!Vq!p(A`J{!qhKe|;%oIcquy#fBBX7TB+2!>&=3m%!F$vNUW3pdq4AmGY
zT?`K$tF5r|SWFJ(xn##GeQifx_X++p$-xlar$R!ltwbC1Q_A{u9SkYtxbc+yLdDx{
z6QxQ@cixg+{}$+e6bS<Rjq`!8>B|s_V>oX26~e;L-D7d5G!#cF;p}P{Z}xwqydx8=
zaMW)S8Ot$fSTbH?02l$WSk}JgzUemlJ%Rzqin78Wt}_bNUQsJB<n#O6-_EEz)(Tod
zLc)jB+reQv<#8Ei2V4S+i?;ISR;c*q3a?fm9=ja@c;y9C?KX%B3VPU&Iz*O`RH4Zs
zU1IP9Qh{cqDumbUAEzf`^%|GxthSivoLJBHmcNe7xr(3lJm2Zt70#TZ$jQmFlH7A{
znIa9@?LUjon${`~J-gfZsEP?2V63FnHh8p`@mHUm)yHdr63J`(DX18Xu3$po>g~o#
zo7B`q3+KV=!u<(f`=ley;($lO2_g1ol~uA6M|J2Pv@(k2O3EO&rc^N7{GA6=$E=#H
zzWcgVUi4XALrZ74u!%IpZKo79-uw-6PY7*RR-||AsjcEwRL@hi!YUdw>6D}^TE$~4
zo+#YFwY|?AcoU0SB(9E6H8vlnQx+keo%s_~s%IU8sddMwM$ga{)jPR*i00c@;X-b$
zel@Ao`aXWG_a`*Jdt)o+mcO6b6**&CWhiQer4<#$8LO@BEqGe+QRv(kM%?(gJMYSs
zdJ|8UTB~q{#&=6I#R)2^s_qgI^;T{>O|UabzqK(AX`u#8Ok~27)3#qv<DWe%M#1qd
z=MRUBzUajcm}SIyTQ?$%#ns9qgfSZ($Pe7hTaP*S(jHOS=g(D&5w36-mh$1KX*-v(
zME1B-?6KdTWP{;rg6K1eBIB{$SXn-YmuTuOeSKujcHIGA*8Ag7`3Fb>1d`=hlBLV&
zsz#^LsZ5QB?z{X7EjT5`r3P{Lo9;{3tIi-;TB*8y9PN|1Pn;HZ;$XsSZY-c!VgdPP
z{x<Gb|5?tn8ciRQ820BBB;KzmBNw(C!@<+=T$Eb<JMwCP!aGy*0j86%+R;tzN~Ee=
z(Cyg0J`duc40f!;Rf8)*uYU*k1lc>&BF?m0VJErQ>^B#1c`jCq*o2}(i|>vSP;h*{
zz;9^WTjff$R9ix}HDMXP;&z|Z^noa{gG}`%2D(k?^fy@eS1f<evmy`aD|k)1S)yPx
z2SAjaf~S_1xX80})G#s#25<NV3N$o=`L5q$lIVIEPZbQR0<jLNM<`qT(#yVtSs`nf
zrPt|>LOV+qxOo=x68CAS2Mm;y8d+S2<R^l5?1?3AC&FYOgHA7AY4TG#`)rxB*w~sg
zDU1iVs-A&XI1Ub?wzH7(D*z;k^<OEoD`09|OvmX#R8{SS?(OCom*?ThdUbBEYcroZ
zMT3KbSLtF<YWI*{){A%OjY`wwv%{-U;}};J6JapzPevw0kR5EkDYhJXNGN)?i&w%>
z?ba<d@4?TKva4S-z4rIZ@JSg>Aj2mSV1TJ7y<vCI>Iv{JZgH5zTlARDF&zDYdP{h^
zR&a15xuleP3%QmQ>#%^h*7Fi(0!|Jc1B6eY|Im>XCiVtlMHd(!6wK}*P*1@u8Z?-4
zr5w3l@EZq4tK8f*{Z|I@y*B1`M}6ng0CPitz$`e+wj&lGlHu0&*EBz=#8%`BiQtmJ
zXk5P|MF<HC4V`@T)S?-S!9|d}vt3GnyCN6-`kFP^3R#f6?xrUDN5U%@hV%pMUp<4v
z9z5`G)`o7+wQJXU3yg$SKK)q2kxjOElSrT2M0@-p{7i8GeRx;z{w|0SBCvbPaOVyw
z3QdSmr_5Qvk^KsAvrvFYZl!jFJ|JwddHwywOhN*q1oo0{MZDfLk*Zzx-^bM*NW~4>
z$j;6a=Hts|PWN>2|BFY6^PE+<!h%gqOr&CDBwX-ixYqfp;QPhwgaqr(k9^gcw)6(i
z34-Llh7}gl5Mfd;u#qT+j9Fj{3s2BDmH6mcW?QGBvk#bmZT-HSmU5~!rIPg4RC5@>
z_TKlkVX1H;drh!+v~Fl$(jcC6gKi@p4sMJ98#Oo^yJ3b*01tec=SK3FkUycJqXB}5
zy`9rc&<V-eZ4PektYNER22RVVaiReXG4v%@K6Lh`Kgr`I;6$%AB6cF)r17{=s6)qd
zPl4}p(O-A;819lO65Opqq`6+?sViT<u7=$F5{h~u)rw$Dr`1x74%<&{H0yeFW=v2K
zeDQxSI8uZVVD|$)h!f;#(Ed9j0s_wa24o=)sH!AbFk#8OW;vY{NflF4{(bEs+^;*a
z!7CVZM!TlA@fhuW6BjqW=_3h0Wyl40<@VQlG=+x25k&u8a_QUYkAjN+TZRH5yc*&3
zQGmP9vPHy_U%n?TEZlz$W>SoE6*e&fPct;se~0hX!;X)jxiNSCRdi#IosMLW*7Qs9
z0nL&3R|H}1x<1X0XYSib>4-g6D_FDiM?xi29DW>~`vf?Wqmv@}>aa=A^tO4N<g`!4
z_?ej^#D7eyshEfYHgt)H%dOG<>E5=6W%zTcemFg~H4(-2X>!9G#B9=jEM}@g8oIWj
zxA3_X{0?}g!EXx106tj4Mzgm0{nrrH5|a)r;3<CSU1X6D66lV(>tKCX1iyX*%luzH
zZU58U3Ulk&;7CDOs=mmzA^eM~&=G*O6JOe4;;ckb^uNL}olf@0l7@bS*fTvAyxYV!
zs9Ao2ul0SbZG4Pephur6I%dd3NXgy`K-f4e7P&S4o*4zj*x1^}@W+=0NN#h^M8Y}S
zNdWjo=qahVLsugqM*spXeLxo(3$z;sgV~YVp-`R#3+N00AQ=!*vppGcU%p&B=hTZx
zj(I$KVV`Ke#K3v18Nb@}Gim^+(E*)m6k|LZbaXKE-d7wDO*T#`*=s@~&ZRHfKmua)
zdKIWo$OQZZ5h*Dyva$G`7FyiR;6U>4UGeb|5ny<AaWE>HOm<l`67sN?JS)|U;os(E
zJln$h84cQH)+rV~q2us3-TM!qcG_AROUTHeeEJDrP1kLI+Z`aRyQy&>KJgd#RdDmP
z#3vcJjn9nQVlSt@>ikG^zQ)kp?Zu`Vy|E(-KKjp)(t+3=1VMh{9E#2i8?^8zpPHKL
z{QSA?=wRZA+2r)~0cCAPB0?o_K(J8^5fV<x5eu^+7IQL6H77yEN-90_4mbueIt5Si
zoX1tj_kRva67;hS{)r!o;;JIlg;9O&!I@z~Rp9_t)sQuYOr8H~hI%y~9U_DTqZ#c#
z&53`hzHoR)IlRdf#WAIj;Km1ECaqW!CLG#T0sS~bz=`|)lPT7WCW8QODZlz?P|;p(
z_(<#Fv3>j5AYwY!;iSmq<DGM$s=pp)yeIf*R&*X?|KPblBV_;Fb)pD!W*(7_L1X<Z
zDn#c?H>>P24a80o0?xFbKQV9Kyr};9gcC3KCWg1vD5B{Vx=Y3e<9iM;&YF`yEhopS
z5>EE|pxG=NI$SWu9IF2qw#%zVY<G@@74LBg5ty!OMUOB}FX$I`dwcsX`=gAEWkw9I
zl=1W3aq?UYQmad8WPUnY0G^ugYg-NZJyInMkoCZBTMPd`=hH6Vb??7Mzih*AlSz&p
zcPxl|7{iQmNj82k+F~i#WI~~9b(ej8$Tsem{<El;?0ia&bess^V!V6dZM<qS)KS&%
zXW@5FxmgXD-yk9<SF^F<uJQPF=bI=O*P&><admn5qev>bP<AG^0vz7+l{&EE(=&{i
z`HDdc6@YCfq@~52(tI*$d9=&uxA{A4p`?L*^Y?&wjgN(Hnu)g&v)yAQrM<se_ch^*
zb{7Jvu_je6<OmA*TdLc#KpMXlnXWeCQgQ-R7AO=s!LQb1c_|f_bQPe|kLD+X1#0Z5
zAu?Y9Ji79yYGN85&<@JgS9o3gWz-M)3b|#x2bYJoow<f9e<DuknZd{X08C2F$oK%}
zZYTJSidC;%Aw%JXW<dv=M5OB_29FIc`A#M@t$>!;Q8f@LqLJ6wn5nq#uSvk>fDufB
z9`&i^i%wZA$BQ?BILTngPV>DleD?lSS(kl}7W$}mmokJwj20E3?!FCMCV(CLAFkjc
z&GNG;QAhA==5}G6c>h#wK&Gbq&N9pW9kS&W5^4var`zxTv-dUU*eU|P)viQ6I|Huz
z`TlQPpKpcNQN-IV36_xi=qS%;e}>~oq{i5Sx_B+P=hy*?MO)%$=H>B5vq)0F#0tT7
zr^)V(35a+zCR-hHf$~LLl7)By@{<rz|5<bf5YNCw+4{{24v7{AQZEELmyiO~W!uYf
zXp>SM+bnZ?362h=9qxb&&nhe=gY1C=@{1KfX8U`_qD>@lj_QI*EBpG#ewMkr1P7W<
zFM%3Ch&Dr!6+qNxfy1&$vz@5Fmn`YZ4mi9T&xP>N&`=ZJ8=FHo=+Vo8m;v+J7)lJR
zD^XF=h=&hXN4EgRZaZ4-&KOYazg)-o=FJ<ocV@{V);K^E0TkQv%{WR*%J8<V{=CtI
z1jjgSl5z%I#4gJ0=Yd1}?g~I3|AYMLkXh#d)w*!7p$FJe4ukP3=RqOZ)~#Cth@vjt
z#_s{Z>uJo9Df;@hjnh|}+1W9@eM{0^Q<t4bhD_3Aecbz;U$dL_`%O0rsRS45@A;~`
zi)MP?hQSzm&0dQ`P&S)pU41T*bm%KiDscWhy=Blr#oS)Y1Im{`SE$haar0dVvUBM(
znuT2r?87Ms)(e2ZDJBB<1cBIcA29YRy5%mfi_L2-R(t`t{l?+*2|EXeEP%4YB(4d7
zuSTYA#fgrmoQ`u#I7vKCgh|SclYoLXf}BOd3UKc8-^<R>(9+hOXl13R4|{ia?wbXd
zFxH={d{Q|AWE{R)8=HK1us8GJOhzkoZAEW<Ir%bb=KPk+O5((T9GXN;8F93vq@?)#
zOfnL&X(a;WI36%;#B0>YXU?GxuVqJ#(HLN)v!9<oKp9Q2zYdgBvT(#I<3UtpS|w45
zwa7Cj>>RoSI-ZhZiI-{J63zw2+atfl3Z@;Cn#u}v6Gfuno%K0epdMXhTn+L4i(`A7
z6j|?cBW)cWJffo1h}+`Oo6B4D3=Ai9v!CfzFmV3ccP!izydf2|D@)eu0fIMg_AF|K
z9q+R!>h;@Kqr4)1H&Y@2M*d|Kui02eq4U{m5F$pFW+V7fP`hsbX#e~A8FNcGGcxE`
z!dIh^4N&9Mtz;lo{L)E`>VW<fH}}j=8&f8XlSqI8g@+Jv@l~W(H!P3W)#|pbQw*Z%
zhFI#q?fF)gmX7=(A|ev0Ry}`43Jq4BZh@<!0ki#ErY`SZeYpqRnTOr4-l;DYiO~Hs
zk~Xk@s^;~I{QRUPVECkrx4o14**154POu5YV>UUXp*vkhoOLH`O;nNQKc*<$j<8(_
zVA9LEqEzKFrgQBY&Vv?A!2n6)QO={<^%EUvqQl$)8-4G)Uh2Q)a`McXmYWq>jy>ss
znm7yeB5ZUt*~_Rowk^*8sYjU>b$&05N-WZsKYjdI>b-2SI9x%xxiBbMYInYIY!bk{
zWOLxrOThC$4n)nu;_RXOzT>(TDm+`+(DDD+eGEs7A9%fg>8&*rcB5UX_xK~om*<XT
z@o$b37{GMZy*49C01-KnDat(hkF>}X{b>JrFsQx8kK=!*8~k$p%6-X7Cb)}(!$(Bl
z#3XW|xRyfPvAh3Zn8p+hWMqyXk#qikZ+R?@evfz7OeN3;Um^vgpjVntvw%wjBmQ@y
z_`Do=g5t-rY1hZhL%oio;x9%sSsOE-bO}>JzoHSbcAA(LWf@G}d1qDjy)^K^5BO|=
zf@THiykkY2yNr3Mv5@)Q&kfw(nfJVeSijcVY?ohry7(TJ=P?C@Mn!EP2WHMW*N(+b
zPfy<s=Ri9D*F@?sn~<O@!_y}Q_Zl<|OfB^V|64WvLvbhfsr81vu4x<v%!ebIbfSC=
zsKs)Z+oTC#tDSB+H!*i=fkNgsU|__2=k|v>n(`!DR0`2H9~n4#-sLuZ5<`*W3A!Nt
zSr^RPWQRp*_-Btb@+HMA-QP~@K135SG7$0T{miS<yMXlRloUaJMU!$t0>K=IQFr{0
zSUZsHvPcB&)N*sbgimytPaUUK?QkoW+D26J@DcHzb3VuUR}1=)GJ1k76vmD|p!=i~
zMt#cSSVz#O|F}*({Ld#a!^IzK44f&)Nme*qWM)(b=(ZR;T&Si%m%qw{^=SWRgracY
zv89*B<bnin;Whm8%Iv?p+QMCHn_=<?r^gEU7VMt?p9-Omms>#Gsm{SyKoLC*MTwpr
zKkXv=v}3)AhSl?Mbu;1q+akJtbpy;6d)|Nc-vXDP>iN_^Ir-0C!!YWXgf8tIwKLoY
znQdGdpnjRp$L=$UX>D(Bo^s=qmuCZl*9jb4+|76OD$bZ|P2Zs8<mO2!#)q&@0R%J=
z_Mm<D$B!RdwMEj`n2#8YZvYtx)-Irj&w2gHYOe|<21(UA0}L_fxU!5DFS}r9=A>gm
z(l?)?TJf#eJPIHnc*MlSfc+Vfjw3$~eW~lozahmpsDIJ<&=!j0?wUB`Gv^!sw?zMB
zsL1X6tv^0I0yMh!m1L0@!?qCKZh9<rc9xd0Klkhn>k1sv<-Oi3`O-h8+tkRwz|zCz
zzYGEtiCGpZrRN#UCgp|{7?u1Dn0W7d7f7}GRSUJprPdExpa?jWeP01wJ0rwppuwmD
zB>-ivj1>$2Gv_nOd`QKu?=*l1KEJq|&cHc~3P>d-pR~(~@AymtaB$;ekF$XvEO2Vs
zG8ISo|BSp1PNO0~TtA8B1*)f!a2WY2q!*4!6JiYTE&ZqdY@DZ2W#s`CQTKE@Dnl#U
zPGj&fYvqXhaVGl$Zu%nyA0V|Lb}0MVh#8Ol_!>TZ>W=#fh=2*&R>+qG-eovPR1uHA
zF-{r{Gi-=0HgIW@6U`wr8eu_6C}$wzc+pbF_K8|j#Ic^$2g3@}J+XHGb4m8c<sf={
zU6_M6;iK7@bRgYAXBlJiKi4RauBc+4^k1B#gI$f9Te0FE>j-*j%aEzi6BXI&OXEvf
z0g^t)wIg#BJPHa)k>jKim5lPw<0w+!Xybv|@c7Y5-IXrSk|yij4s5u-OeI{o{gsv)
z^NBJ>rhj{c3Kc;3?vU5o4wYIX*n*P!6R466R}=^hmLwCDeEJEZ%sT-^9wcB%^+pEt
z0EdA)h!Z^{SA^w-i`_N_wEunxOgOntpT1b-T+@LZ*KD1}sLL7w#x`_>fOP$Mot=f9
zoh(Z2b)P4cmwfkJ%TfstUWuQDd-IFCS?e%;{ZIGD*zvH%1LFjx!VoaPbF|VTfi=gf
z5XcHV?g93Ur$%Nk7eMW~_zN<>-2R&=hF~=fdTM$W^YeveB>z{(nsQw^70d_CShr{5
zc%-G7f$J&D?l;c`(8gb0_j+U$|EbaA*Ctq$JqMX8GyK}ZDkm}xHdp_EqfKVoWd;2!
zoT~OujC%g}<eP8~HLBoce@q|eMV6>S6yrg?_Jd_xxA4rd)7XK5h<sAX|42<heW;-=
z4&@;<g5-Tou*VIa=vMe<&*k1O1yqu#inShRG8c|jNc2C818qqEXo|J_=ZxT#wNud+
z#}8Uq!vFld_D@o!|LVQp^l_nltg5dA$f%B;b~I{9y5l&lH_#0oYjiDs#DCTM(d4mj
zt;d2I(MYPC;^tl>2HW6sMMCUED_P+y4yfOR(%@eLgevJt@BdLKpvzhcqxz1n?AX2x
z%KxJV>TnYhCgxZkr5E$(PIt~{fpMjrkhS$VxAw<v@{ilgC^QE9<v3$L0+_1*wVtTg
zqVdZpWq2Tgs5vrR<H=wAe(f^RR+b$6%1$PSR1w}1psKDO0d#pd3S4)xUf=&TESu$_
z+4!{joF~iwL4yhs0XCI|g*&jRU>00h81nzbrb5-_R}C89BCarEA-xO*rZE=~xt8m;
z*e`2p#t4A}qW;z8VQvMaY$agdMGHY>qGs;&IJSJ%<0xq-^S}Q{oJ^-e7cO=jpn&Fu
zz@q<OexThaKa^Dobo5}DY$zw5L@o9r<A=FXkFQrD%|sA@H$bD74rpjT>z^foAV-R(
z-`aAk0F%0D=cW;pkdy?lct=~vSB>BMh$4+Z*PHE10scI|b{@DvgZBj#D+m@gXWf%7
z4?JTa<+g6G&cq1RbLHokp}9Rk<C5koB;X{DUmx&pVG$VT$@2aZz><mvxJ4G2yAKfe
zdv;{Efpm?M?sBy=+9A7ghw|aXzTSL6jm&d#M+%VQ59y^~LiR?uhAHh1NYB*FudX&G
zW0AN5ED|89LwGDjY2r<<*5sFgQgXej4v^B*d&>=s%0zGO{Jgx{uc!UXTp(k0CQczT
ziQg!)wN<s}Hb>z03#cd4f#nU{%Sgzz)rh!S;SCHZ{aIMwfL@shZKowXd6MIw5@TqP
zCTQ{AI6F5t!?W=Ff`GL2bzlw(r!QZMIdKYlbl3kZPeKE(O@3>XCzJ|;dIa<lqTodd
z3zlxU21bqsbx(<#WY!56acWyDT_<95u098@AqMf=m=6ZN3yUc)2LJ;vbUl}(gnq$l
ze-GdasW><&6$`2W&B|n~Y>U3Gf{TkQ$;A}qs0PY-Eu!m2OFSU@0=C$kQ-cQKyl{cN
z;pnh-$zH<5z>@&5f&z^|{B-R~lVyYhN33>bqGDp&fJQDiQIH=A49c59ghoXsVb#@A
z2)4~eX{7Qte}!MNk|%r_-ZpXJ5MDxH3v7Pgvu81o7aXr2rGVBEKL`)!K69p{k!C07
z|5je}4Vc6GA>dsI__<$+qr!ysGIs|JU0hsVjf{C}!1%WgNZ*ip5*1!ABZ!m#%Y@d}
z-@Y>k?)+pVJ8_N(D)@mzirLv&wDM2y0OlXJq~u^8H_K9&3=1$_K{nx&lU4%8r3lJp
zMVnRMQaG$gdry6XVILsCed~qc1slM?B^O9=jf{!Ghu2<&K%v5Q_U!9xV^9`m1s?2@
zDLCdWRnSA_`s;627I*#ZNAzVFV2%hDu3E3peujQQVc8vE>Z)G5MguQSz(HVHhZXez
zl#<`PDG^ju21m{VOnU6!6KI@5O1t+F+}=|R&>foTh<gbQ4`ii-BZ!lWt7%AiC%M00
zKL9XY>C2RWFJllivFeQH8mhd_f)tITG*Er&d)JheLvjNT_t&+}yuRB*%dG9^Q_EQM
zS}&!?%Qwqzk4TWQ%MEpA?M4G%nHJuqfw;~0a-DPjMi!Y>+MVh7`VihD(E=_IHDQpQ
z!Jcd~NE%-7fr|h<6%eoy<8}Tl1|BW?298h(ZHxv1HM1NND`cUI!%eu<q|{|hW*@pU
zM3j^d25$ZoC~fHM>}-SzHyTb8wNiK}T8(RET!@hz_rV26pyuZ%lOC|9@wFRgr2Gc$
z6Cv|RAo))}F?DHzw<C}v@S=*-+yGpHWCHC?CpiQIy%icK&@LX<7k@Si9s`)S=t}{h
z-jW81K7e<pjEM`iba_5WObh{w%mNKRCR{`Mi7_&BSF32akG^vQU4&k)mi5YHQ_k}~
zee*PU_ri<pY&_^_pl|M2nSUAQ7byJM!m&OUru*QO9~=j(|I@+#@9y^h_OBn0BwWX%
zW7%YF;8h0~8N(ueb3)m&CY_%@4X<HQYY03*>Dn)&R8&2wASv2Og`Uj|@Qu}*-%Q^?
zLHzGZ)$nQVj61s<Hv)kFZd_`Gf%8Dm`DGN0Lyo`q7~YY9^68zv6*B7^sKF#oD^)6Z
ze%|S2)J0WKBJ|eLpg{sA<wR(|95NA7#7&BIsLYWPu9;sOB~OnCXwTMo<c4Nr&;Usv
zK~o>#Vo$wzz%Ev5r>Z~b24&4oDEL5badBFpqbIqzPSnd2D%19z`q#J+V>`|g5`KxG
g74QFNKlT-vH?FG4F2+8DH!2|-YFetr%2px&7x3b+KmY&$

literal 22155
zcmdqJWmr|wyDz$EkP=ZT0SW2u4rwU`Bt*KE?hd89yHlhaB&EBhySqE@Si1jvpL6eX
zzn|xPSb}TLIp!E|$L}TJy_^L46Z|I-2n1b9Qd9u~ffa&4V1khm!8a_bg!<qIx2>3}
zt)hjYt%HuW0Yp~E*3#6%*3?AzwY`D0jfsUhD+3z?E8S~jTU$#T9!5s9|Gt62!rF+j
zKUH)HEP`Swsb&L#pkhNmFp$J#TnOajm6WKkl4J7zyo2j=yN9mh$peZcqR8+!8J8Ft
zD3Tw9uzr8j=9R<ZuQy`s3OL^bzaJ^d$=~USVLcV=@O7jSL&%PM`nocFA<FFZmQ0`d
zS<8F-$o|`F$0Kc4Ue?=bOqAO}hbC4%R<t%DQBl$Mw|j;-uSrQspP4=p0zY)Q|1bJM
z0*<Dz==OMj$#;J?W;$0J(d2fki;HMrU=aL@JF=koSK#pQ@aoK?yx@I%^W(k6!|mcD
zkyRrC>dR8wZG}|Fm4H>}#zg{dYjTD-R8O<nY8_le@Grt^R@SH?f!jUrriH_SB+jrn
zE%yYCdi#+A6~+clMm04xG&~mX+k<+{`bP7+RELM_O;)!X+fUV|Cic{%vD$$4O!deS
zH|Epj1~TzXUUs_^SkIolH8*FtoU_5i@cRzdyRH?;MsG|1@xwnpKAx<2^YogEmX?f$
zW+z^&fJf!C6+(sK0PNC&%VEdv`N>|z5Yfq8&)KpcF^k=fQY@VstdO%a&t|d>Mi}1-
zGLEL>vXsX?c1C)-=hT#HMc>o1;9tiD5t97a#Kc2uJyB}arg+3|XCqeoeTEf;>~|2Z
zSE1NM?vR6DyTy9W2l`(Y8o2joYmD}1s&X41%HCgLp)e4_VlxNyt?M2w{dT>WHVVdL
zMf|~)e0ni!v1-dB_3qtDgai=@4-ftiu4oRk8Me0~BFDG)!Nh{85BIk=t0A1_&gWmi
z1^5=1!^W;;ZX17p|JQtc$pP4uE9cG$Tvnt$K0dDZ=aUe91B2skvas-|sPA2?%R?Ry
zta@Fc21BX*b5>o1g_@0tJyDdb9uKY;2lIW3^88|l3yqjg#Es79JbNyCe+qC88!w4U
z8cv?@JD<I#p$TOFI`Nc%ps!G^;_9IOFkZloCnF<6R6^nu`i|&m_?DTfuTDGpd8N~)
zT(2;QoVPMsGO7eyH%}vylV4rkNZzVf7@}he-Z9DLMTHXbmOGsqTHDxM&n`Z6IxM<%
zbrE~?UmdSb|FWa~I$6A)8N{$Mp>DDMM@7qEBvZ`W+k3dq#^~YUvKdMxS=n_!C771o
zbL-;{9GhhyoXD7%j>gLcX(uOc&5}5sHeV}10#>IJeSxbbII!QQ7XOdymv4=Ysg942
z?^cN)Uo$gDY>(xg4)Pva0yZeBs&@OL6Ku^?(JJQ3T(>o1VYNJsh%(<E%@Oy*AVdr6
zQZ%^6mgmz6#%J$kLTkI)&Pxk+zrVqoo}TvA!R-XX5CQ~u2wa+~v&n->G#Nep0Qj$J
zqjMFr`<-JO6B-#m|NPc)#&;cD-w#Sk`e(b7LsX26oIt7#2a`?u6IdzVx0UL&!IrqK
z$Ee^lHXMBecSfY7n4#gbb*q-?ip99OpY%{d-v`7dxS*uz=Evdbp~qvr)#bbcarvDO
zjwTZ##w%leBO?NVv?0fv?Yxi_UI%=BmkTxpMMd;BreMxR*Rk4@URvny-@d_Sc|1OJ
z0<LJ|`s%oz4sdq*W09ey4XNz!4koa6aqU-*!D^LgH80ejPpF5qwzg&+lr%KVaRIXs
z0<;~zdVj9YCfx$;XtgVh^Wh|}8CSDpQc_ly-Eb(?tUpa~5trLK`-6gl?$y2fYW1u+
ztETI*7$61ochocU$A|k)_Q}tB;O#OAEFF&E+<+RvD=WHsbG23<HM36GRSH#glAU+*
zy$*eFv|LBxnRMR92xwXUMi6&$s{5d<YzQUe)q0#(=t&6~X>*Q<HYvr)?Y&o*&F0{G
zFzeuJ5)zmn@z3kEf5W1+Ev<$L=Bn42SAO~P#4|ej`9bY!sK~{pC6{O5$oJ7v1%~E3
zYakH~A`^Yx-Ah0w*MK0xf+f5%MEqEc29s%aQ`~R1A+~!{5x{T}PP*(*S5Ts$piJ%C
zKU~x_BP=K;ahO544jWFdj=PAbn>_><T~E;?A|lXmnIN8E5vZW<{3*b!e*|h#T)Xl_
zJdB9YaWN*#NMdI3=f{HX!9m0EA0HsL2Xl2Z5xFEHzDt%|#$&nYai6VGphuds8G1uX
z3Y#?0>9m>Bb$5LVV?3M=lje4TS8u=Hx^B%3j0R+7Wd*9GQ{@Jz;N5;HwE`9ke7+cj
z_*a^>TbV(szig>pF7{9g$C^DnlI19MbaX0Br%!4h-9!t;{aQq2DqSwkKUEsTv;e~o
zxVaukTMVJWNFk@7h)hcA17E@t`^MayZQFD|w6?W50l{$stc&NdqY?4LSWk61;MSjY
zyS;!>QcDdFx9m<%=!IateCgY@%3XYUbL;f3*`paEo5b;kmp2LA7Lk^wwO;4#4S#ZS
zf>HknelZ+K^jSRI1Pd*XNDvzUrQu2$Bs(a{&i0;~ni6vK4op9h*wHH0hUtr?hf8m0
z;A2X-Z@N8L1>T5JMbqg`2tNA?5HY<LPngd(1MI+KTW+Q}tXOi@9(NMJ{ocL2J(w!f
zTiU(4x@tM0kWGXjJ(?}G-`)DFhQNmVOI;i;4qYyK5XaMw)>`Q$a#{?wVG6!drKF_v
z1G~fLG?)4G=~K-9WU+>Hjrsgfx>H9!Hq)taqcpeJ*4DQNjhC9aG6~k*cj3Kt%-r1E
zUYO&_$;mBuN!rG9waoLs?DT*Fdl_6`UjqYLVYjOs85LEKEUc`g<UO{{x8uHV&+GAc
zyU2ao&#G2yNdoT9uv+c(n}-Sr6%`e*d(WOJ9BZvae4DV?CFZhxJ*nwprmZF?hw<J@
zd{XQ2M#cQsHpRwJ+K?>gd^=EYp${Lhpw18a5xhHs<D9dE1M-XRy|j2xZ><HXtE+>2
z++YjnJ{}=q!opr#UuQNMCBHe}?Lxr$3~2$Rx&{uaXJ`nCra0ebOqLVI@}O?B+<yO)
z{oWKz%aGt>zcgc`ZMQos3yb5?ZzQiktf0XZ-qq7<3^hh->Hy*Ih{wRm!oDCb4Z>l>
zc=Zg`*Jg-6?oWYA1&}mIc|}FtN+9jg?L8U+NATR-XZQPa70A1H?|`<$iiwGN7%xgm
zNdcSZ`*_>@2*<VZ;C!(s)#i&1r4EFE!&LkBVqd~7q)e~tWZEe0gQg}%%V>@?+_K$h
zwiNJ#Iu+Afvo(yGB{)EBPKN}CfT{NkY>NjT4EN9H0R6qGGCH+#@~7W622=X-^HTAV
z3rMZ+YzbdSL>2x*C5erVWwBn9gqk;{x~D5Z=bUznYLGE8DcRVfk%$~UCyLbJj-aAv
z*{S5{$Q8zafh(8CYWY&N+5H}B(;V-PyQHM0+stdQU%u=FHb_oKhXk8aPJg!A40>V^
zXn5`InG!jzVF6MUBqPMe#dV!eY7GHP!Mz&H3MFaXc5c#CwHZN{c*qKHKE)Sq0>BCb
z6HDC2gv)A(29|@TVQ9Gmjy~$-#2OJDU&-Khv569#Si@mn4ETM(lwxYdTy0!LUmeT+
zs!0~psZtTm%S)WM$sA_*R#sMk4BtOwiH~GSU{c7Z%IN4U>s(&$mNcJ)dE9+3EoJ)U
za5(tOZnySoB?yn#o(@ERY!yaB6u^@T-fzp_tuuO{G+Y3|)dD+H1VOQXXy`^+7f}{)
z$Nee1ahD4&n0aGTKoo(v(nEtRAPXCUkJr%9Ei;o8=tP@03O1~f*Bc57YY=|0*lrEC
zT(%w#!MXr%?sWb~1qBsV53tm9rZOOb)hIGPz8k8S@tfqN61fm^Qb4vi6@!JLy3N$S
z_5f@=7JYvwv4CzVs8i5&^0=K{g!N0B07`3UNCCncraBrd?gTcJPoi`d!|B4GUG}R)
z#l=_b>#rx9A3p9UXp<^<Y(0$!5teEwhF1XixlRT7`@<Mx_=CRTOQJFR<Ku|6jo9R+
zFFTCq1smfPi65H3h~NLLyBTGTqutn|{R4*&!z1<f?JEf_DcJFnM8zTKWxT)l{_3zM
z6Jj+QBqt;L?(dI0XU+*CAx@5@fxU8Iu*m)CUU*KFXbk}u3+G_k1U&HdNgpHkZefK?
zu?)E3TaSWY3(!B|)y@z&EzYkK!NeYSBkh6E^aL&TiO_t#10_3q3=jsiHf<1sdR%P;
z0<I`{s2;ra7ks!Pla6C}BOs7^c4nKIl~o5qA<X)w$=$Jb4lJw)=S~G1PpBV(_1d)X
zxFv|wu%E`76r51R*C>wH6YyCG{NE0<BRy1Tjo+{iI}D_IkUB3J8fqCOTS3Eq_Ih$G
zEG!h3K7{m|1?RrKXV4Am$0owlgWbUX%bSE_dEqIlk`^(OIA=}9I>U5YetyK5sXDE2
zsz`afql|vIcVqjaf3%U5frh)Z8Y#_pcT@0vqUiqli)qKT2qx#VqvIC1b<;(2O6s1L
z_44t6P*TzpB-I19t3;j&y!-QYQ%~)wYjK9@utU=J)wX@a5*$;GQkVz5yMs!YPOoN>
zw=_gtdr$E{m3hd&qvKyR3n2S-pt&NnsK=h={wMh6qs@7a5f7@&8aWNUPfpsEWG`bU
zD>XXh3n|J5YaeX9zD6Ws{PiWst3I3S+>*F{ITUX{k=0Ij`@Pat`6sIll$mPv%-$=y
zT&FU`&5a+EyA-j~Nl7stt=$EqqlIj3{}>Q^ylQn=<i=%b@9K_N2-Kz^CCwd19{ykq
zzjbxDIT`2Zx-EEg)A%t~tNw_GLBm;UX&3Xxt-GJ((Qg3pViB!(d`+M#hD-qB`=P8z
z=P9zHyGO@l$P)tQ>wQXNrKO+tg3}|@dtn2Gj`xfE^(TJomwjQ?gUd?As~C2t4@;M7
z6IRnB3x@2srB-fX%_qa25?>pNVz!LR4FRJI82KxDk;7q!KTtsk(}XS3T4U!1w7(;E
zpmW)s=Ck#gbY2~Hu2#8B6W6C1?8h;?joaSYdkpVbuh(-OZk{4v!gnT@;&I%V?Mw6(
z#tIj#*4$lPmp=NLekl)lv9BCX9P{W`6Jpg_zP+>WJ;h#V%XO1b2vriOTs`wVYtBh#
zyA8Z#*}Ko+dmOXL&a2EAVRT|UtAiWb)SUGd#=Oui-8W5@a?e%1>sq!VxZ@cTncdKI
z6;Q427jE-KbQdht+)r1e6dXvp1xoZ3pM%YKqJOA22ICnvdLrlL>23)|WYqoH{^n*E
z@hh$r#737}6clFvP{doWQ-;1ioh}SsEsuTeAtK)5O4v^Q!<7{S9Jr;<&eR`Nt@E1J
z>Y`DuVsNeaQzo3PQc7<-<Y*o%2=C;ys&TT*C~VjpBX8pW@L)yoh>;0Nd?=CdMEf3}
zRQzUb@?%$}lerFSU|*z3;+0~I$Vav7q8^5w)|qVA$ZGi?<+4A_DCJx@%CM4*lkV)a
zo&4KECvh6E(r1jd<Rq*E>p$$6i03!6{?W*36~{tPZq{b)4S2FB-8MzfH*CbncqzHs
z^9N<FQ9$6zCk;76E|AFm`4FB_N@d&+Y{951|9@hwj0d9t;R(EX<+(RIW;|8W^<k1W
zhgqQ2-;nn`bKV=jhr;y_Xvwu#i7y-AaG#Ha4$S#@v6kLHWII(3ZppFb+b(gxsx5FM
z<o*#%{!Xe>7!NNv-Ba$$Kht{$b7((oeke9J&H5T&aCwi!D~*~Gi&D)yf?#bYO!oT6
zDV5{uepL12uOn8qq#s)BDq7BlF_3H#?U#m&&BSTwPZWN=KWdf!Qyf*-@%r@BcP8y2
zCZq)e#G-c?q2pJcGJ~(9Sbv(<(9XzJGR6_}KD12BpXHc+rc9^2{;agWOHM8t5vAz*
zZ9(mR83CW@N)Na6OhZtu4~;YhF=zKqbkoIh#+8l6STu`Wg~<O0(zR#famG}`Qc&55
z>t`yGh^LciiS{LglfrhU(=y)+7}_FYzko`FF3A(2mm7nUd3^(=+*bUqtnhhV0h7x`
zEO^%$m&CfbZ4l#&ki(^F49-JL$F!1P9v!l2i=wVp9T_;y^Fx_LLGfcA7*nqq=t6mK
zvzIG~*PVHbab3@sIP;>47DbSVRUWFhFY!K~))bi3v7<D%$!(^jY?s#1-|AHuCnm{M
z^unaSU2@_ScujDXv&^vaPL~>;el>|mnhMiy)y7e6QHLHO0wJ2Wk1BwgC)-y%LaM>x
z;d5jbep>kDn#Jcb;;xIzK*jUDDzf+h$@^T0zRp(`cO4tjDRcq`d6vgSffs&@zt^l3
zr^a2evziIoO6}UH(CPK8YTg8Y;{L`yFv+}r(z7PEi7>eAzV%b0Y_=e^oJfq#J}KpE
z{i6S>@v=l{(7aC8ZW`~&Ss*2*69YA4K&lCUPf%~x=0z&n9NSX||0Ie!S>uFxG*85w
zvb*d0AWZYKj^BRQSl7`!($r5Fb=7VSYDH);w3@cuW4b8#9N+G6X6hZ)<LeB~g}Dux
z-wh<Dh2IDIzY5rZ5Q@2_!m*uRm&u?#2BM@sN9=cxlT&Ab9KjLod4FJj7Hw}XJ?O$8
zQM4F4_Qf;msS!HE;3?7{ANM~T&4+55Y7Clf4&L+_Gy|s<`-+$-DTMlWYh*@p)ZVj4
z8&@Xrn&Pzs^3g-`-XHGUZPNxMsO8X1_e^4hB>j;0<OnoM0y0d7-oLZHZ)d+>{mprB
zSAn8&LFPp4>b-fE;o(@HIDDx(@K3UHxrG!Ci!Mwz_t1@Oc4khu!V|r42nwR`N67R9
zb*_4XkjY$iQ3va}UwYb$w8|8#hLSThAR2j!37r<u64?_&=A5@^G)sQ_9^Q-kT~p1y
zm)qg>!6mV0c_*N^)Hx`6OWUg4Re2w-TqfHCJLOM|WZxt?sO`{4x2MTFt#P3^BY^43
z;YxTyQn_=eQFd=r=Y~pdA<b_BBPqI8^W2lp)Iu2^bFi87=sY$xr0#C4v+%kOkN(s3
zR-|#4q2O^AzXGSX_tcg^I}F#75+$`q{#B7>PqA{ay9XOd)6<o}RorrS<g%PHHagR8
z&jC2(Q}5+6m#uQf0EXL08v6^I%@+4l($lHs8DV`V@~@u?b?o~72$3<=zfQY0*+oHr
zP=A4issJhfp)1dv!jXd5vY-lgcx}dgS%%2<YPf<}X^vTX?R_36L9Yj6az}Bdh96%|
zt}QC0=@~%|a6a7_zc*?kDj5>DMxVZQ<G_z;$EqR2@VvO4u~|Mrg_|w0ec9>}Th3b?
zf8T>Jb<bfvyRIl;O^gv2S2o`{TBj42-XM>azX<5?<@|s>xz3Oa5@I2rzFTB>2J5UF
z$f_R7b@MKd_*eHK=hRtU(p(YITT;`#L=8_fc|4saH2jw_*czJ5z2hXbp%`jbF9?b(
zFo^BomL?6GOAlbbvz8o4y4u`dz9H?Bi%Jj66p;J&Ff_0A;g=zjQSpwj`?#E(h6~5V
z4@t$ZKL_rA7VUf-3JmPtn+*=*ZI2Q>f!*8H;9}MNT^i9)#w}Z+;eGwq5*JB=LQ#WP
zZF<${$+L>B1?qrZ9+jwCcQOWs$cBbz5LdIA6^rQ|{iZCQ5=}P`1Sz-e1PWp4#<K@n
zX*@1k41c(Ex`(fOAM^(3q~J5Nws8|h<A^U?!yb$5V_L&4iT3#uIqH2!IkM=@Sk;Fw
z`d$iksF`SWJxboTDv=U?4X{<3OYQiG7P@#Bh~uzi;i1IFT`-}?=6`$N=kG5@1yPgf
z+&j0;K-x_-&GK6HNZ$P%I=PoA>bc@a^v3mq_eESou{iEgDgF1ya~B^4d1Rk_ek*<Z
z_SGN?l4dv*?(aQ@Ow&YDskJEsvK&!_&EJz6ZnvMUv~8pP-oJQOZjOFYNcT4G-ruGD
z$5N9NRz5r6T)K8zSVvwFWu6*Fp7{Yps5j<A^2PXWEmDejQ_pz-BwFi^vX}8;?89Wi
zf%|2N+X+K+|2>9^vWRbyK!~zhk#P(t3-K+{`uR0lT^H^&qo_ZU&*YWp-(fz&A-u-w
z1Jda!He{A#)09+wJpwT`jqWfQpbb^LC17W%V2i+BNwn#*8mL=^P|+Tp_C3agS7n43
zYBgm#eBvc_&8X~=%N}SI)qV=mvY3_VMCzjoJ64>S58|JeH!e?WXPx)<7Kz$2v^AOf
z)9&zwlrjo79O1cJ8e;9yYFB7VI95vO;UhA{Ba7T72%GzNtKL3Z!yA16+&KpnE%V!N
z4TYrdaN4nt4Y){%h(k{2+34O;WFSvrX7j+!;s~nA7@AVhX2qTu%*vkOaed1s%BbWX
z2+)}9c$Uxk+;a!JCaHUqLT|BIu(bQIVuZVK1!Irb_kp<aqH2w0(LVXGq|k982SR?X
z5>FOvGn<CA|C{}036BR)5Ao>dn6zA!0I(t1CZDiLds`(3rP7@=MKa4XU!zXJxHbk@
zYpba;Pmf$b_lqg3v!iiI+W+leW3~Tcax~A)fpsz0{}miUx^w&Wd7S9|=`>BBJvT4*
zep6thl?(3aDnWZ3908`l;ny*aTCu=!STpD3-{Wz)FiA}A4wp_f^hB6tuA}ALf?bz6
zR_2PI^sCZ!K8Wb{WkI&8^JKN%hCBjQ2~RjMx&)ACAsM`-+)sYjA?y)l&Vl%7imP=i
zqD}ZX+mEVo{UYkvUMw5`iO{*fo<s+F!dxDDj_aP7BRVmvX4QxjoavXcfYai{PK%QF
z%VF{@{3ow$0$(tHxSk3^PGiS*`9Q+q@rR(vH$oP7`p(1!KhKC>M;;A#L|gP1(_I;E
zldt1^Ba>^-UZC;YtU3EBH$tz__blY4X`{mm931oA0+RwSIcD063OKM0^jFAthpE;s
zv6)vFqaV(duQXm`M?{Ih{2(ejTtN49>1LWp<Bd=7xMuX2d2f~EH9AJ{fy4b<!g|0s
z{z7VF&qAX6l;Q!c4}?DS$W__`FU<Xp{C47y(!C}qR$cvR+M?%GtLmps^0BWjC2(Iu
zS0>@YtlQpAk5C0=ZYf+;4ZSwf=E0&}T-hPM7&Z9A=;)GW4ZD!x|0)DzccA9ndoW)g
z5Ef?C6-vl=+>Y(-=a-~0fugghh~;@Fo5XV3<8-&}hk88i+Q4ecz`1Bu(eo!@oP|<5
z`^_9yM-&b}3=ZMVT&fm-wwuP_1Uo_ZY*YM(nyl2IG(}S-%<=XBt@6w;jKH^No4Cqs
z)7>|-?%B19R3F^X64UgSf-A>3iuoflHZ=1!vIWYOAie$l`k-z=r}67~{rn)b+Lb95
zgv;+-Ih~X8w5jsGaFv9^Z9Vh6_cLT7^~Hdqg@A#z0qMjT`I~48>I}3@Mw4rFwdzX=
z`>RWZv#&2x&vi;?rS5vEJ{_RudGSW3&bp|W)=r}mdtG8Naz{Abt$Y%;Iz@h)#A7^7
z8&NU1<=JL+b+qBY4x4vcIus;{AdQd?GI`Lnp{AiB<=}{ojC?jzV_{*hM%jN**{RPG
z{G-lX?)bdZ?2=_A0>d!wjjmG>L1l*BcR1|4f;v#cWY3|iKWC}-;mi{*3yQ_lkR1Ff
zru%_3KrA5KUc19VTTB%nA%Z(v+%!+(VTg@>Uc}psQl~nwLXdDkJ?e6w<p(9%EGcEs
zy16e8DEYpmQ}ygyZ)HLQ{TPzW%uEbuf=@=?ZZ)+zP`Z)F?`nPh_S5`GiWTR1iR#vI
zX?o8<yS~OtRSPo$-kNNpYs3gr1?jufG$uQmSMkyNr+)8OOj_bbil+CCW%Z@VR!(QJ
zw31c#H}J~v_JS5A+--U2qap(c9Z}GEeMZl}A-^Kw$V?gWo}xeJE$PNmqk))Bm3o0z
z6^p~c=eKX)#`8I`*w?3kFNo}uW*j-Ma31*lSew6el-sM<-uML;EY3I_EEPnc8b`B3
zN(SWpb@Hxy<j(3Zj+To#@);dIU{IVF8t#ZRHwX}aRmqDgtBPL_YI;La6yM$oKXYg%
zVe4dDHM=IU7DX5(D`yMSAxkjwx{iCWGu$>-*p~Hs`<Cb@HZI$r4sKg2rGMg5x6O~v
z-;0YQf?Aljw^D;yTwljRql;|u&yD0;J_uW1AUd7R-gHPs<qL@5BYjwL8Qiv(L~EG;
z9Wx0A62D8$w*||K93+deWjg{>^%cX%8Qt<l4{y^?D@Q(Kd<Ac$5PI0C%!HEeTR0$E
z9pa2icU6M6lwY5{#4_ybB={t$c4po%2-|A59^5@IU;g1!4k8&%VC#0t(*6P&u7%oK
zc9{}{2ef3aKrJ=_kdB#|*>t+x2ikYqEB{icBi8}nmFpyBDm{=cJ4@a>aY#{Ii*D|h
z^m}!7m_%(^qMZg*`gqVVv)YcBS`#r|Uu^8cFnL-(JAT?1Nim>GIO+4$f({)uX_&48
zE<=D4D;4>Z!P!syW_3?=d(MM!mJ4!o5=uixO&2U#^Hkw(9|!EQQK|0}9Qc)$Sa?|C
zZyno~8ciZr_46TXmRyck%YFk4tF>3I?uqLZ@J$cPQRtO*^Z{mq2XHf`ygE3W<ma3v
zLRicnuivb8<69$Ek4Dn;$@3p=UQU$sZHx`YEe~8`DL)nd?uL#`i}|MT)u**u<V#=d
z75h<h`Lv$|0tp0;@j+>oKlmo9)#QhC%Fqr*o=U8{jlC7I#N;6iWtvp@TLwG-J#n7U
z9!tn4Q2!VgDB`$1iz*3w(Y4=YJ{F@dR@iSm?k~twSE1OaY|E23MAD~Gj2mnAX`m@t
zCUDG5pWc3H8YzR<y(2D7ShX%_YLrV?dc~h1<V^;b*VrVQFZ+?$ca1CECSA#m$#xfq
zr3PC*vB#Us0JkX8E-9EFp=yskG(46^bm54zCsyv|p5=3aj|cDYV-r3m`j4}kJ)0S(
z$Uv`z^CeU@KBG9PqxLSmrYTKbG}5y%r+e)CB7fT0DA{X)c#S>P3V%X&BBgZ$w;bj!
zecShGINwbAK9Zv98|-^R?7AhlC5m!A`?1neYFSonH69bhkvA%EPiM++ScOGJ<&cv?
zB3%yn^{ud%6w({|`|26pB}ORKx2qf91c;%VgmXN}{chp>@~~3%-Stu~n;>JLSDph!
zZ+^k&AZ`#IJXIG*Tt5iB7EI{*>d$B@bGkRtB8UvvV9!|8xzmq^#VoJth^(@}k9I)h
zyJ%5*(7~vBd0-^Ed6}W6tnPfU2eE*&Xu~`XqWXG-Eq+DBb6YdoOowUu-hTQ;^xQRP
z#bV&N%@ZLz$?lt~rTkjI_ZQpu(}}COBKBNK1pCXA(XzEeRO@9rmr0*<{3Q|U=fyA8
z2X&8DOt@+q-nmP%9aa)x<lLJyHKWQ}CyAJMJertm+qWv8=vHIhAAI?BDVMh$zOm5+
zlRx>~^9ys@&`_j22dkr!%Ucmb??NpJTPtbEr*rfE{2B*R#`&(PCO6!^YYG)wQU&y+
zkh{wb-NhJ;mN!T*U3%{tE-z@xRo1bR@WeLHa@ht=BvzB{TaZr}rsY40(&YElOPbXL
zDVyE0-rWgTZWY`n_XOzV(+U%#vX;Q#w|=zYw$VXt;&CIWakE50P|=dV>Ec|nuQjP?
zqC{UI3;p_7CFNH4?F&=B5wz?mQ+iwV)q+B6P&4HIcHi5SF{kZ7Q`8aTUeMgA)!hEg
z0V!iy@I&LxXrl)y3O(9hOx`@P@8TcitmU3YxespFL}M}O9ky=Gp-4;pZ(eYI_*_*L
z?JM*p%htIAlc|gaD+0SfO%F$9Uf`AaIW~Vf(pPMU6=r;$=)BUd=@^X&*{#xgRC?>g
zQPMnWQ$%-9e#Y2RYSOszwJQCtgSO?Vw)G^Hv1%$p-i4Nj5PxLrSh}{w@)(oD4_|N)
z(m$EXe`YAV#UQt;Ntzq&WP`t`I!pKz=ie=*SF`GVMeWRgPkz-P#1k%;-zG0kNkwfe
zoWUDGte5yyeK>-GTh^aCB+qEZ#7kojar8I}r!j3`YPTn3-e<nd4@<s5d>4o44clFJ
zmY6R36QL?q;b`6?$6Fl1Wx8z)p}l<B0U9aMwQQR-8=?c7L0)~Q?{FUYaQo=rQ+#--
z?Bl}A&@Ma-mZmpz953-__RNG7+@GO{^^iY*gH(Na2T!o^C~Bw#qn>!Et2Kt!o9K+|
zHlm7{I`hW(Q2q-NI2zsDJ|^G7`IJ(k{$j|`#wt*!B-I#y|7%kY0(w1lnBn!$OLKwi
zz7egz_Nvxiyexf0BKDDfRf?l)PfteD%wPN#r$@|++`|7m`aBJXb5^v$;2Zr8{faCr
zeDTlJ_hjm$ML6wm2;JM~EqSN~H(mWF&L?+Bp)dY<x5Q+o*`wBeD<Wx=6ERwz7Vt5^
ze_WFzDa1Tro&?Jn{j+8n-(J;6(^PwEwUAd+bM$3#c5NHx!uFD1b)WEJ?T3=`@CGVR
z)5G4r(|~tby(p&?ND-CixxdK6?vNwhOR|~@w7PkHv-#q=tkXjDB~6zE%#W9-m)=N3
zH4Bpjo@@h;SXlXGC&wzI28)`eY6;D1pCE-40}q0ued(?qBJVCK%ErtoE7`mT>v_kx
zN|-}V@!{^fxk_V*1zlDz%NfmytBE;BkA8owMWh%xy?b^ifTR;zy3{P3&kQ}_okO1I
zLCApqm7>T1wYX?D11f6P)q`JwTd&>Hki|8+;K8%+`+|<D*Uff?izn1pY8jPti{Gm8
zicAt`qjT$Iy#z`}kV^J+a%@oZrDmx^BwLIpJxgJT@UyS*M(Y{NbkOB4c%$(ZAU``u
zL5@I$%-CW$rSlGz=Teddbb}&phRiFli4al-t%tV@1%%RCYzz?G?HMjhH}8k0%9dT;
zO3x0n<sk(4NM&a`6sel9mX@w82i97Y`4qJg{UUHUQ`T+eVEz(E?5ER4oF??(Vsl($
zjCXcvYzM?t$tfWYSsvc~uF{pwZ>1^E>dT}!2mwKBYPrn*uU}5c3gETi$so`|Oi%Wu
z`#K)kA)7SHR6`JQGV@G*+<UDdx8-+*1HV0{#V?L$FUu3(>){zS-g;b73K);pmoWee
zez9;Sg5>DDsoV?qmROb$&9*O_?p8nIuAci0d4`>}9r>}{LjH$C+)cvy!sMPtqhGC?
zRi&hS?nislBGzAU^IpddrB^Q~K5vql1^6*+`VpT0oUqoVg8WfPa0&E>?|m!Hlo=WJ
ztI6L#zQtV7F({-R7cFZCe(U|xPW_{j!E~|Nb-(+)9%-zv<;{+L@~;!dyI~6~*%Jch
zdJ~eoJ1vJ9JHfh|{imf#8lFmwE*4mj^+DZWDh<82qS;Nx6S*+pbljY7$CTxfv8!j?
zCXrgqy^C%RXxv#7tY-bUri;mD);Z@%;+|jfzZL4fhL@LQ7T3?7UiSx<#qvhuc+Yu>
z>?G5AYHUyaosC8I{lP=>u?PXnkZRXY@xEi<=w~aaZ8}4lM$@{^FAfUJF)c2M)&|s(
zG5n+sGtGfm<h-+f=Y{JUsvG=+s-h1x!lGK-mnEHLT+q6TZM)%W;|?Q#l~23BF}u9T
zxi8lFIKj9i-;hv?r`2zioO=F3HmJAEz~K902}DT*%5gPwF~|vTAT#L;Pf^hwvMk|O
z+_dgK<>f8qa!3!ZXM^u93I~5Hf9XO#)iI^XoXj<38`^=W5l;1(?ZICd8jdti$;%{5
zS!9Mnx8{oQLJ-Zj5&o@+Y_T|S>iB+eKk>GS-S(G6V%ImLkAJTbTe(L`houZ0O}p&{
z4gJQ;r*34=fhbAi`5?IySVj{%iD6-t1tX#?e?GPC&|!865SHXRE5~H2`}qq5E3xHq
zj*!ync?uQKn5&9lRhUHOA;M=Z$a(wj9x_Gvw^K)%2>YJ?#V5^(JrR+Hzc(s8svbO@
zD%Pm4-T7Gn#Wz4e#~uoh0r;{cWkoA<X2Hg5`tKGz1QL=RCc$zJVjSq`$4Sa@{{E6~
z26d3<FJ5$Hg$eYwc)`KMgGMwQVmz<cURIf+l$u~90&O1iMzaIt=|(ZvDJ`8$m!lXs
zI9nefY*1JjrZ;iT>;S->aj!=!$`nzTm#(D!ZZc8VR;$e;g^2m?wkm^zP$q)B)xr(5
z;FE0zxeeoB#KoqLUE1J+NatRJWo6NGN>Y-NT92yTjGb2u39NSDXz{J^fVQr;_p4c|
z;Jz29eiIdVgMmm*{w*9>2gr~}f{wf8`*AyEr!>k)XJytM6=;1oFZB;$EV>YYqhuKk
zrFs*s)AZHVz3Hm-h@<uG?{5{XE^p36OcIRk?|<tmAQ-X!f>S6Y*#6=8eq4urCl0cs
zCY!GX_4%JcmG0x)WCKhu0B3UvCg2?0V}X?7=|H#|${=foF;90eJN7NHqn>^>Gy#V@
zk2H|*O!aths*px5y`{zQ@^RK}VVbJ^LTn`@tRH#+&j`-;Ow}I#2Zz-wWTE8)T+?f(
zy@g10At}ddfOG*`lMYcr^}fPLKG7r!a$6LzcG!ShvNzNd6-!b}1T1RI2G_Z0;KF$$
zNB6g*jh?IUVMT^u;hE?fK;It)#Nj<REPSbh8&CCKAVIosCe~}H+6V)`fqX9u>76c1
zD}4*+DcQUM?VTs10kshNF@)#%xl%A~D>D$4k>iNwf4Oq04WSueBm>YWn&E(T9(d3P
zwPvA$BotPrKligbQYJ`!#)#7KLS@-hAu;FQtAt>>^~DkpKH@~<wns~>mbn-!eS%<N
zZ9E6hDuk(Byc@0((=l|9DQR3#hO9{#tbQFs-Ru4FS2hGRWR(+D&J7DlatL9%1L(*s
zmg5Yg$-#q%)ZnFTHMPByMdeAb;W+y~S#$ZCpm!Y#AVNLwPJ;@xh|J*Yo`K%>?t2JJ
zLj8|d5WxqUzQ*IkRDjU3E(e?L*HC&Q<gTx&ttRY9()bXms~`p?TADAO%m#f??V*af
z45H8SdM%;Av$Vrr6Qdf|Iu<~@8)<N&)W|>)ZA}SoGUZ@$^6}8N3@>gdU8TUKCTok1
zMGQur<(ti?ptFvM&K}>dvH{a#ahdQLicheEzWT5jS>UhA{?3BvvozPQoH;a$fLgyO
zx$1Mz{*5AJA>VnA=odhr)<i#m{V(U&u2d`!$E1O8zKOwfo4%m*qJ}aCJ_0s;nkMhz
zFZKZ*dU#U-^^WgPlj?k+&<KC<1rQH<rT?rgV|Yq<MTtBj2rf&Osvaz$0+`Ecl)L~5
z9bNDXT4hTBo{5aqT}YYScbDi`lmnGjtO#w8+n!vz%B1oo02D7CDCWd7>mj2s*B~Il
zg}06K0J;~pE5H~JB(mfB{st&iD6E%(Ar!!l#>W%E`^osm|8rND#>L)r8^B7{9<`#@
zE&CDx>?8uf&aqcf_?3`;=V_&j5d(*mG02yn|M7!rrqV=2Mg|Syc5|k4eX{=9aoNXj
zo<cw%GCW+5mv7y=<CPtuU-L@ZYaW3c%p$EcwflQwR8V&SeQ^NxkmE1l1wakwvn^pw
zO(KCbOux3@sr)X+e}4YKs_7Y^=gyt9uG8)Ar{mjkj)Cbms=3cN-z^ZFVdR$NF90p>
z|FqZN`Bx}G0)v89p`f=O$~1C-%m5HU^QLP<#jn;^v>OjxS!6OrA^<z_4!jRC$g@ia
z{Z#ZJBOiZ+Pd|#cjG_gyScV}j`>i<S6%y=wRnzPq5SIF@`axMF;YqrZB~uIqU+_Hl
zlzF*h1N#&!@2>`F1OBURd10mFs=)o%A_%Xee4J^;Ju@}R{#tomVE!p+=&3q%xQ?b>
zMca4S9HCqT4}K69CxWCtt@$Sn!LZ>0ffcmHxI0$=<ONi*1KwE&!W?n&{ac70EY!Og
zh3-k2J-7A_%2yz4qAtp&aIXbv#{T;o_=J2s$6o&EeL6@wImO}{6xLGMVa+MRK6~*(
zecEH$+@8{$guG#e)L&dADhQ}ySv(Jx=f*&i!?@kb>M8*LboVfBa1|v~1aqvXfLsQL
z_gM|(>DYpS^+%wD6AF~D!+ng+G9{Xgp|x_FnwlxVKB8kRoE8`;yFg~bwse%a!F{nx
zl(6psPSt5GLL#(w5Qs5e_LtDG#lw;lJ1bQzH(zv)e0)InuRpP)(rYXhLucLAf)Ezm
zZ})|NZd_gQH_>PrbsltLtuX!PHZmciFh4&cXaB7&<IYe*X@DZcRHLM!>GkgYGI&_P
z1FM7^WuO2|2<gN}ut)GlsRHGq?%rNqfC;_Z6ntEcR+Q5@+Zt)_oG7aQaohM2E<Df6
z{5uJB8o`bfQUwLKwzrEG*B|ge+#jxu%o~oy0cNouK<<om?by%ixrI>f6BCqxX!zUx
z34olb10<sB*@#5L<HIc!1ePnCl&GN)a)r?kGZR}0Gs!=|b=hl0p#sdJE<bof<JqY6
z+1_*%fJmkR5OQF(+3ZQ4;C%o<j$vbC>)AW8>%NMITeGR<KJU%gcu58x`v4vTh|iGt
zcw7Lm<y&yO)ZlSAFtwNJ>gTdV?r&lGXGniy!x&GM>VT5tWBaykqDHUP=`GE-Fr8Ck
z16e?1Znd3uJ5K;k`^?yi`HBzTaEc8Ux~W*ed78~@fO|f@Y<^6cwP;QOl~-ud^W=1Q
zB4r#JeTnv~08ZxQrMV|6%5ftAB$~cHIe<>BCK#nM*B2%p@BW2)P46f{k%udh(hMXl
z<tx6K8ozAwZH&CG@A0p}4w%(b;^8%@85uDlNdt&NQc@_}+uQ6jT{&@@?^IQBA)uVe
zx!}0!J>jSnW8`H55A1C_n;i`V9hb=yim(C!3l!J~1<NmaA$_R~8<XP=QMYJ3C;#YZ
z+v!!&GW-gdL#(m!d<ff@aVj3$Ehv%^rWOPPCS$p>As1x;IB(b=FAR|G0N`Z=ps|!0
zqiw7-n~-fD=p;&RJ~qr&QO&{%z=|QI1Nc>?*(}G=-0flW(3`l=Cw`Z9^LCRFk&)kq
zHdnzdK<)woo|nk}{_08+z|^{wYWnQ9?#TTbSB?lTcG7BC&a?gp*2aBac<=+PAHun?
z1k7~)MID&I_(KKB$<BrVoH7@P!&IwHr?KGPo104w1oF462a>Q>9EGoII&?I)pap+P
ztNGb>E{!4o|1y0A*hXF*U0nc%(>$6fhWe?-Tw`5cb`Vw?V2|%F7cpHM^|~+s4A&j>
zx*OmmngBEt5b1P004p63ChV~JF*Qu!#vcIknGO4KUxfy?DFBr1ZQE@CfFJ^CFaQb+
z;!H4#(0{#|=26~TtgMX@4vl~OO?E^fu()i-ProCguRsys54VST@@WFWguK<_QSnic
zkzm&0rib<<4EZyXB-F>X)7h;&ow9&(6bu$5Ih}4;4X{tfYsd`{*4~AQ{}naGJlJ6{
zu;B!vPCRB^_@15~zO!Kw!#E`y@pU`TetM`G@7-x9DGd%r1Kj-WN9^9MW|Yza20CCT
zAE%q#c#A$&vDCESGd+i{Cj&%x_-MJ!1ccX76tbBg3zUJZN2R2M=-~DzvIhatNYGdz
zR&hc?1M?+7N@amD1EU<eK${_Z3yt-?HXtZBjJDl_&HJ6C?hDQx0~FsDAbFX!TRgv{
zeFfwSFW{ELhXIC^Sp&$pTkR3tFo1V1y|5-#+8_#nMgsA<a`5SZ*bhH;l@?xCO^gAs
zFpyQK2?1gAE4vhnrfUl1m}H2Th<2_rut&NkcDfkDfJ%K}S_MfdS=_vWPzEzb>vC(P
zt?&dINc|TF@C95QmtMYHUtjn3@k#hRQdVJt0ll^jrO{&Q%>yudb-gwKEQw~#-qrPk
z4_=v)2gJ4tf`0<SmRtTr!89)d4EgoxCQ^O!s%}B@eIeAd0X+hHo8=a4c0>Ev_8}z`
zfJ8}k6M2}Cj6wTq4!|vK4;NAZ@Sl$e6M%tRGQ56F6ug4@i9MnyDw^#MwML-3RH)UQ
z3ZTl^`1tSA1l){Avr!0nY+sX+we6jJSz99C;(0j-c(fA%jF24n`g8D!ZkaT%NeUt0
zEH|I$!Dlnp)-~u#KQTP|M_wp%nm555i<A_}n>QVM3Sah?$c_I|SZwv_Z)GQBnG_tr
zhY$hZ@B1yt;RD5g4^u$}K}h%uM2(k+q3Sj@{%c*p0NU@|-M{RSBW@EmMD2%B*ahKD
zf2REMYw#cIe-Ku+=bqmVz*-c-L4c9s;CWcTmVee0Um7vJyqslF@X-~()}NhCPW-h&
zr)npPjyvP|w%el}(UTL%P<~v~;Xqxi1Q-@FnJ7%4F0tSpw*I&CE`_vKFj`>%g1ul|
z=GF$%e~bU(apJtvI|uTyWXqGltcNK0c*hDHAi(ch|M{pwXrJ_7hUd(gL8YIhj|S7r
z$A`DTZJ~EO?0-kgwkg1D<tCXw;bUNA%*)PR^It}K%~`V+ui5t>D+A)I-Ak3*hL42B
zTGoy`YS6^&e{!lE46lzjcc(*YAQ}V?bp;?*F?PNpZ9ur`rkz1B^Xaz&nNZB2z^y6t
zqo`SL4d3k^?Dq%ocJ0apm$chdT&!>ZFKks8A0hoUDzFD+e@*TO$dhtF+G04Ubm6-_
zK}y(f8F;^+@sHg>u<yeOli{)_poAAoYZZRUg(~yk53<yMaAIVF#=uxm{;I8$F!?F|
zGc!+wgkK^H@t}jGqn!=>N7?CRyok+TdyoZrt{-FLIjp7)HTffXmf;^$5neuf`NjHg
z+>iq<b7ZXy$NkN@;w1pLF+w3e@&B>|YUpcTy{`Xjs$~8yMJ5!=l=m-Kj04-9#_WfA
zg7Wdd#i-(z_ERx8^sF0y&+7Bkjus87UQmgg_gH!w|L;Xmv7(_A=54bh5&h4Bme?Hg
z{#_M=UWE&1CGGzg<w-+@@V}iTIN;jpO=;HH5>$9|A<sn@tQjHhcfas{0>KLa;@iC|
z@YHk9{a=L20XSeC7Ynyk^EXL}?@_@1aOW-voG%$QHMJuj(dgKi6PT7l!NkNI+*JvM
z{JX6eK(4_!)LN{%`7#JZvMVd2#e!ZU>{rbj5JAwAuqr2?15pqGt;%UI(sMJ}47+sh
z+|%8i4ji4Zi(7MzHAXn2aS1gbg<ttNN$I;qw@X4`mh^!m2Daqhf48$s;+b`@k!js$
z=8sK-s5X&ExVV0Nlztb|p&(cIuZUl9Xa^%n2RD|Vh}CLE6eJJ7!32~0lpPBzYM99)
zR1s!#A4&ttnZ=%quCZ!&;dNCm-u(RK-V^b!HSk}EWMveqSRVLlFhTN?QS;T+FSQE8
zJut#%TyjWV!gXd~{Hzj?E}za0xqsXC<Tny=sr$V%n6%8#&*wX8ffIJQZ+Z3lujna)
z16LW3d;=qKNf$Rw)|mD*e|hHb1bx_4K)!>7Ndo}oMcY3EJ}EiTqCo*MSXit;mLe8i
zw4YV}6DA;dq7Oh7{xMt%hRPq-#?$sC6hog6<>2NYJE;7A0f3L!Cv|_rvcJEx-2Kh;
z6$pSpMJy_E!)8kj0}>l5K%S5`m*}oZfT<)P70~6quwJJi0@JTc16|{m9U1^C5UB1L
z{xD>1NCYbQjBa<bWPS0`9j0Vbda>yq1Zzl3fwT{AWn}twYLfVG%@`Wb%C%#tDMpar
zKfQK)@mm6_jg(q(4D(xHG;97>j^zM5=h0;If;d1<)!tta96LAq&6{8BIeb&ii3cEF
z;k8MqIm|~y!)@O!dGs^rbJt&Jz}y<i!r=uS>%t20(HaYnrkfqqz`!R?`&BB&)8#MV
zkWxF_Fy(pl`SWj~wD`e?D&z1S2Me`suN=AQ?xfv)4vqL=eZNLJ%w%?Lt#fa>A_=6o
z!5}<Sd>=jpra0b#(}1C%3dijnoZ5{<0)R83EwKN$+}4uGU-!Zc#<N`TxKBPS7vCiS
zp<S}{4v&jd-~Z*SbC+?ReE{^A!UJeOU|(6N9$`K=UM!<l;OxHPWU(!90(FN?e#ee{
zV6KF+G<4p)iwUjTe7?0eMvxf1XBN8cLw?IGX<(q%vS(|p_zP4@+E<U6Y&Hs_<KkA&
zot>6_;<^V0431Yy9FIFZum}jG!N5lljz`CEh7pV9l6J0q+LM;85k1WM)NwP8Hl{NZ
zSNMbkpRUT3z85y#)rVSw-x+`zD4Ul5eAb6fnc3i+_5TrjhRJ0{toLbKY*(*Y*pNU-
zmaY6A2+BX(_yqyT1&<?s!{6W{0c<(n`d7^Vh^MdoUs?Jqc0c)+<^I}QAl1Lu{)RIV
zGcBOZusN7AXE&+QaI;gu_i(i;lP19bql6gP?WlAy(VY}9ylgXT##wH;{7rA6Yj4^D
ziHPyj88w*%25;{D-h91ZA49#rx_MnH^kXvi;MC9lM<w>xGh0w1fDXR2`C%e0fpVk{
z)N1Tb&~qU`RaLU_PlgB*Fa~y?Z{zuI34xfhK*WP_JA&~Z1vcD5h&^6_haar|D5e2*
z1u?hM^~~WV2={>1ngn{)9;nM$A79FdlQQfUi%Bf}dzSc>Rf@d8wLch%i?CW{pk%j0
zb#l4}FTSSJdLXN}&pACCd(<Dv9FmllenXN$0>-Go^d3b-Y;3R1kSqw`zTF-^B4JZ5
z^Z<_GvTm$=(J}&V-*mAlBs#j-bTL(YwNn-{RZSlQCOf`^G!RT+*;B~W;`@<#d))8h
zfv^f_Q+R6AcjclPYf!ag_IU6Ef%wVH*OK9?uMTqjcCR6aO}7+aYBIaF);}-R83WW$
z8k}rY%k_^*s%-aX=q)Kh?xOWpQ<DLNu*r54k{~;<c|};_0jePGyFrXi+F+C#n&db#
zIxi_}Pkp<LBqde0JE+ULJ{t?$9?rmy)8O!uiaN-xtc;h6BGTO%Hv%C59)k)QL+#Sr
zJ$qioJb|{8bpVRAWRmAUrvoFhgiwL4wOR$SyM9$knc1wez~deEg6pZ<=VDytk|wgF
zxoGMzn?8Dn;YJrs_v>{^AlAKLin`Av4}Co^E!O7G$4OWw_q#V>bT=NAp*CEtLK71{
z{@r)PltHj=0@pDH$dhF9<WlfhH^Agm-B#%#KLbPR*W$VeFx?I20SC}I?|dE~F6ni3
z3$2oZ1Y09vjKMTAoqBaJ0DK(&`0Hn$t#IW|Uf<s<?N!6|beX5P6ped3E7<&td~8~8
z&O0_K4F#LA5NjV=oaKy=LB`k)f+c2VF9r>sCF_px<YfQ0WlAuin82z9N?xhphLzKE
zaL@1cz-2H%xek)!Wl$vn$+&X;;|to0(O-52LM0%5m6eg<Vo*T?(VoHluNc$$U#Y-)
ztgPx;)#>^NpyX-PPk5ODPKnuHXx>~N`n2hkj5_$W$(3kofC+K(H+7ag=zx}>PGa_w
zp2t?HNFt1A4otKPq0xV0gQa;OBw)9Wh^Y$VFuVElW9U2q)bLk2g08{jTP-M3KnH8V
z0BeRyNgHv+z~3TU{z0yzz~OIN;YmM&jTFiAb&7Alvs0m?JFMq9TIMxz$;rB{txu?F
zPs8I>-WnQ`babpge;Er9Y&9%48@%L{locKx7(PA*V6F>{fT{hmMF8`sFppPVdSGAe
zUoO+ahvUs=M?obIkVj{Cf^B8xGq`uO+RR0c+u$h}u<F{kuh8$|1Yatss)E{#iPwYq
zY^->jZ%7Y#=&+;~<uaB&$z-t!P9QC<u#nV~<~+@A;yT$*`TW!ccz!rCEs(Fb?Vgt#
z4QY~+t~?Ak=YMqnP^tv^RBuo3u;4@q9UX1;Mv0D&w!B`CBW8xAy!YCLAZ)IK+L&oa
zV2-~2p5AgR0vZvY_xL!y>%;ZiXV@BG);|)g%i?;4oa*8JgqoUPwf;iI{$Nh7FSf|(
zbYOCOx`MOOC9QRUQ!A`(d1*rZu+^~OhS+hodLNfr_c^AZD={d7WT(GQgDN6Of$A<7
zken{(IZb9Nxu>Vqzz8E>X-e0s&P3rP7~n@G6A#XmO|pkhNe9GfrOJtl!f$W$0z@1L
zxi`m}+`5GB!6r~pxKFwqvc#ntjyu0rS}b~L)Q{F(%n(S)nY*qeefFCU?kzZ#stP^l
zOy6;LNo3zqbAIFtj!D5`eVDD67j-%pce^%YjsKsKJHeR>A5@%Dhr?8njhpe8V63Hl
z+|E-b?+2LJFu}vSy?VHP<Ap`V90n4BgaaYSsL2s2sofAzcPSGSlNbXLzf@2XS|eVN
zP*7lz=R58XV2};<f_=|K$c4U;YsCrXcQ$Km9J3F`B+5{rQ-gnlq2FtQH#8tru|5Hz
zkmC9{v7H9Epmalpk=Zh5^e-Ea0gFJx&vEr6PKynoR{DTRT@7K=6^j^rnw49M;-RnX
zWp2SVD~yU3|6iv8bsW+RbxWYB76x2c50@h&$hGseKtV_-jHt`O$O}A-!bb%7Gcq#L
zRhjmZ{?o}?)DVl?4H~dT!eAI2kLRp^^=DDjh{@6KY>+^frS}Om#WOk(Ij;_`GB<P}
zF9;ldCo4Yfp(H-;rDbF>pIgt)H78+YOapzgwDmp@eGn3)y5FSHeLBN4G07(<XVYJ7
zY6Ju5<RD{qI;f)tA(`dl{iF6uJ1-u;Gn!uK=g<4QpdJwg{^&(S<Y>%3m?xU6MS;v#
z=bV7Q<}h4s#tY^Z2fa~LRxQ1cN<8?%U#7@VuX#Q?<}m#8qdK(bvoZ7-o|*Yp(`Em~
zFZ=z${5%Z!rFn-%{juDT=QudW<BCJemR#|i78ug8bOPD2pzR2XFPy_g>hH9tfpQs7
z6gq_ztLP(uC{-V1@(Pe=Slru#{0_EDx#|Ch_yNJSmw5mGegN}aXE1r%YcldXDAH?J
z(82`x+r2W&-q8O#Q;7!|8DVBKQ8?zJpzs9+PJH%vD<IDG0+lP^z_UdB<RI-`l$OBv
z9FIyfGPzvbwH@%%)6<uFc!C*sM54T0bI{?)Dg0FRtfoG>yW9WE<k!(`z*`e9g3Jd^
z@TXD09}{>T3Ys&Jjlp}*j-X}%{dp*f@6SEZT~nf>LT2|TLf#i-K%UDW6~kqtk_7sX
zV_@<fIT#MQ`mL?43DrU@>*qne#P@WFxb1BFacz*7Qn}s@q}$U~wERcssr>bD%efl$
z7-)Fbi2q|UKTZ@>x;XF@sn;O3*e@x=s8^fjy4_+R#S-3RFx&5S1QV*?lhiFH@?OFL
zpB)zRTrC9l{nKfTSGS`e4)5-%(d|%RVYw)?T!ux&BzpU0=cT}-D@i~=am#?DA13kd
z(;<11H#taOCm(tTQ)W%3%h^GtiU;I@xekYlwbvWFR$Q;<oOd<+ydyvm!>rp*MdW@Z
z@XK~vQdn4E(WBWLlmNj*GUmxay~Svyi4B<2{yF!q4FtYlCw77F^T8q$M-cbmvEOU&
zW0VK|<ZowZDIm@{KJGdN_FgajAv!u0G?edGXMv9}8T0+*GU-$?jn+1t!X5S+R7m5i
zZR&I}rZ4ur6t~OH*_1JEVvlVOd9c?15d1lp4wd=u#PK$ZUmchUm&=3vs^7p7G1q&i
z52H*AD{-UDY`mWp6D_17(<263CTD_uvoS(a`g#;vBe0wNU(K9a)>t1XY&xw37|Ge1
zSfc?0Lv2YS`sOuO#IKEiCax_0_0I;J_<z+Rkf{6rP2wc?(*f(fq=x@C7XM~vNml9q
zPci2j4&}PW@rha;tYn2&PKi?6aWxS+?<mm@+i65XIVEyRlQ2ZBNJ}jukp?xklv4*J
zN;xG%Qz20rrxFU$5Gw2cu=na}?+^RSzOMag-np)M-+2$udq4O6zyHs#onA;{-uB7W
z>Jht0a{XTt<Xi1N%)@U?Ad_z(6B3|36Vw)Kr#_{Em0BD5SkUO6UF*KCEW)t!GZ+`s
z)j50Cw^%=8O2-khTl&`1jt>e+<m|){=Xb7(wx~b%VaTP8Q+8NdNW8zNPBH&J&^}rE
zu^Ap5JrFBa*fMx0Fk1QyqGe_8|1YDRb@)&&D(>ryDRoQB+t(Q(;!ynNIKL>eeib~h
zxUFbEG{zUn>pJ&Io)Y@X%l8-#(g_?mYY;psXkWOGEo$pf{dq##(%QP#EB}r`OJ2Js
zuPK63HZ^!2>)YeHx@4Kb{hE@3^GDw_?0y(u9o6)-&`Tq)<joIIHu%R^3oKkHqo)_K
z!Hd>s{gsRQ=i)s(XFQiZGVZZWsUd@Vu?pV>(?ykAlBx4kR;j$Qvinn3)HyAl9W7_a
zadvYw@=_r*TAQ*t1)&p`0LRpm3>I0ygRW{Ps?W<H)E;*4y}Vx(C4%s93?oPyZieQo
zLkn~?-0Het2C$_dc^t*_I|1Eb1hF}w&C?J$+G~e{&4m;!M_&k0{Y|8RiYqdMB_&&G
z(l=$P;<#(f%8n}URBz0-@<8evO?43rKhv0yup)G7e|3YcfPjFBId_x4|A*Zz#jw4t
zRPeH`I5pDnQ9L}QH52VjqsD(UPyiRmFgX+R048XpLys?8EJd;`03Cm;y}dXF3g1fH
z9H6RBB{>FTuI!bjb4i^0!XIJr^ARLRN=|nZAftEDlZ3^@EZzs3rl^6~qwHtc#PxcX
zqe)pTUNvuUhyJ0#`AUwTc`^g^_ieE#^%X0l(oFsRsng@(({k(A$3MDI8lR95+GAw*
z8^+i!^(p#|zX|;m#|`R;C|8}$E9Y0tv)(T2SRS^xH%<R|U*q({3x~{d=u@;@`x{=i
z!5ss-A@unbrohQ8U*Gd1QtQ*G#vjJ}Udgfb93Kos6216H1I_=KGy7}<@}$mt*A07P
zPf3MPT^`WFjRn&S3XWKrOKlDaSP%43e)-pU$2*gfh0?S=C6t8Z<cCe!R;{=ER~>j$
zPc4;13(W*if16Q_-ZB-3dmZ-;jZNDtdeeScO?@JIORR6Wb5+pS33(RlBZvV`ttAJv
zm3J1s!H~%E;Jo@c$XJ9ypbK0BI#vU}uS4#uP3qIiCETq^DpK-)sqLK_N_w1S?gRwN
zCiM7BQ`GmH(do3r2@OcTsDV6C>_Qz@SU1^t&1J1)*kLVHudJ*g*5%95r}dfUQ)5#6
zye!to)vJ4IrMbz;$=lD5>5w=L@(bDs<CkCTh$U2|ce0UvaO+g|1tQ@I)cZVKc_}X~
z*)C{%emDB{qeV1USaa&1=A7}ZAw`%PK)O0m>yXw~F&j1uUV6WbTeWqmsHhuV40;k4
zE`Q4rYbsC45(|LP1L}USL=nctS&t7XC;Dw|2yRDCPR{1+1;$vbO<1;`JqN{%dwtLZ
zt7+|qOO!wnn18l9Ws8gJ<;}7lX1Ner)@gc21?^~-T@bJ@nu94oTX>hC50F6Oaor4^
z2mrOk9C!*0=5l#OMb&??6QuYcYDs^Y<f)bt;guTqdPdRMJkuVk3i$p*>ClWi`_qgt
z|0lHKpX`e7KXwe#mfs%+MWbNv6IpR}@8|~iZfK*_QkH!$0<4^p(vK9x2npXZVsGH+
zj!Q!O@PJN0<Pk*I6o+ih^{T4LK>tW+__#hw)uccOQgy2niI0zmlUlpQDO{J#>&{<4
zPXIVFr?lPd<TB;u8a}DXug_n+&17Jm0AgNg#8BZ$mD`8~HI!$At$@dnL!#{1&ArR0
zs;>5DD;G<C@E8UVEE8h|=qeCSs!*MJj+8FQtK=bMdg7JVDR_n6WNrE;(L=Gt!JK`%
zhGv(MD7kzZ?j*b@@U+N*mV<$F^r20&9vE8{GVB$h!<&ddg$ni*G&@CN!B|MQE384U
zcwZ>6BQ*fv@UtBczZNCn9>U+MiIs>2xr9Tuu|y;moo|V-axO+D@tnloym>Rd#k;6K
zSl#21B(Ps!em|J|>IQm95>DV>bqs*AB1WRjJ5C2|D!(EAKf|t=A8OD9bQ2K~nOp8B
zES2Zc{Kd(AdB5GRbVKL0&+$vrdos%sK7zlz5%gitfs<E$Rrh^W4>As%bN1=_+^{KS
zwEg%LCdb|vb5nL?l%TROv9`8ObR*oIk6A##5uV@0i?X04X(PjqX>jLHx6OAX{F}EM
z&gb4{&M5<Fq-?Cs3!>Krkd-LxaV0sqUpp(K=HDHg`huKaa?E4+yzgDiuS^Odv%xN)
zaA^h2?-urjGl1+{e~wCcT7T8ODiFBf-CXvgM|mS@eu05%;H9mhS!+YgJji0PIz}gM
zeI8+-h>5xKv=nxTk<n2|t;deNeIX(^eJAnE87sHLZokaK)vzCD7@8Oxcc7X38p~gL
z@s$703G4nQ|BMyr9|daZrf1`t0#p1Ew%rrGZ*#}Idurp<_O~U})*544R$dYEj>-%j
zG2!J10<W>~S;1~YxNi8Jeijvdf^A{ND;F#+Eh&B*B~t_H<RQ1@Y#WM+i2-CQqJrZq
zIT)fSBa=}#0z?^uBlQ?c)zYO)DI0lBR-~<@mu9Z3?fgxqS<->CDj%}7u&_war;ScA
z3w^mvoJe|Ch&O%(+n-cAB`}f)F9m{J2fcI-A(<`L3cOshrFlJMdg924$Ug+g3$g%M
zS{5kU=FL@@RClcvy0I-Mks!U^a&Z6OHf0N=4os!DdFJda7VN&aYd<Z|;UVM_ANTCS
z8aO|qS_bA3OcHK|NK|D8)571F2Ud?XJRJInvpEhWBRE&!yWE<u*ig_vK_YB~cdtRA
zU9FktU6&vq(M!$)FE53cqw@I9J+nyCcSmNB`sq^;mb@KY_I$|I)phu}%jqY8_2H%s
z2jAJAqILo=E0Zy1m!jrr0J<WYuElN~zMXeNHE~Vdi0a)0PH5}I*gye$@BZt6YX^GG
zd~S6fSMZ3)-Br3Qs&W(R2c?5XR+;_5V?Z}vb7L<{+2B=!Mr1N@aL$g$6GMu?q9Qmo
z@QG$z8R({Hf<IHUJNySH2G3^EqD8eFPVdJ?^Wv9crcncSND?wLGYPb7R?97(7p8(&
zv~5NX-mEm+Z}|7P`hArBk8doRJ;s2PHr*9Q9rq*{{fNb&Nh_>8Z7nk>63JEKVhQzg
zyG}(n!5l1WIZ)5kJ&(HGuhlK-nt!(u{Kj#?#>H*R;_NeloWvz0Lsk_y{h$(0xL(C%
zbH5B!`)nJ0tARUiZ`)1zuySI7ML2*n;96_u31eN-;x07Ww@-nfnP8BEFJb`&hXC>l
zC){)FX?w>O)-!Yuyf#8iCNwxc_F(yR7-s4^LJkvF6pouTg3QgLed@oO{~Ssif=2H3
zGJx7r7AVWEO`Vx|ssd)(yRv65k(T=Z?PGL0_O`}~>)WQ80K-yDwwP_sGjNXl1EHf0
AzyJUM

diff --git a/doc/pub/week34/ipynb/week34.ipynb b/doc/pub/week34/ipynb/week34.ipynb
index 2e33438f4..a67ffa2ee 100644
--- a/doc/pub/week34/ipynb/week34.ipynb
+++ b/doc/pub/week34/ipynb/week34.ipynb
@@ -3,9 +3,7 @@
   {
    "cell_type": "markdown",
    "id": "23821382",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
     "doconce format html week34.do.txt --no_mako -->\n",
@@ -15,9 +13,7 @@
   {
    "cell_type": "markdown",
    "id": "c469d11a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# Week 34: Introduction to the course, Logistics and Practicalities\n",
     "**Morten Hjorth-Jensen**, Department of Physics and Center for Computing in Science Education, University of Oslo, Norway and Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University, USA\n",
@@ -28,9 +24,7 @@
   {
    "cell_type": "markdown",
    "id": "aec7aa11",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Overview of first week\n",
     "\n",
@@ -50,9 +44,7 @@
   {
    "cell_type": "markdown",
    "id": "a5cf1430",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Reading Recommendations\n",
     "\n",
@@ -71,9 +63,7 @@
   {
    "cell_type": "markdown",
    "id": "48e8c897",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Thursday August 25\n",
     "\n",
@@ -87,9 +77,7 @@
   {
    "cell_type": "markdown",
    "id": "15954f7e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Lectures and ComputerLab\n",
     "\n",
@@ -109,9 +97,7 @@
   {
    "cell_type": "markdown",
    "id": "c4dc19b7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Communication channels\n",
     "\n",
@@ -123,9 +109,7 @@
   {
    "cell_type": "markdown",
    "id": "b0ebeb01",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Course Format\n",
     "\n",
@@ -147,9 +131,7 @@
   {
    "cell_type": "markdown",
    "id": "6472d61c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Teachers\n",
     "\n",
@@ -180,9 +162,7 @@
   {
    "cell_type": "markdown",
    "id": "3f3971e2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Deadlines for projects (tentative)\n",
     "\n",
@@ -198,9 +178,7 @@
   {
    "cell_type": "markdown",
    "id": "61795a44",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Recommended textbooks\n",
     "\n",
@@ -222,9 +200,7 @@
   {
    "cell_type": "markdown",
    "id": "4069e392",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Prerequisites\n",
     "\n",
@@ -242,9 +218,7 @@
   {
    "cell_type": "markdown",
    "id": "790fc0cb",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Learning outcomes\n",
     "\n",
@@ -286,9 +260,7 @@
   {
    "cell_type": "markdown",
    "id": "6d0591e9",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Topics covered in this course: Statistical analysis and optimization of data\n",
     "\n",
@@ -321,9 +293,7 @@
   {
    "cell_type": "markdown",
    "id": "927ac19c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Topics covered in this course: Machine Learning\n",
     "\n",
@@ -348,9 +318,7 @@
   {
    "cell_type": "markdown",
    "id": "b2e304c2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Extremely useful tools, strongly recommended\n",
     "\n",
@@ -364,9 +332,7 @@
   {
    "cell_type": "markdown",
    "id": "b3778a4a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Other courses on Data science and Machine Learning  at UiO\n",
     "\n",
@@ -398,9 +364,7 @@
   {
    "cell_type": "markdown",
    "id": "9afcb9bd",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Introduction\n",
     "\n",
@@ -441,9 +405,7 @@
   {
    "cell_type": "markdown",
    "id": "171edcce",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## What is Machine Learning?\n",
     "\n",
@@ -514,9 +476,7 @@
   {
    "cell_type": "markdown",
    "id": "09e377d7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Types of Machine Learning\n",
     "\n",
@@ -543,9 +503,7 @@
   {
    "cell_type": "markdown",
    "id": "fa11b82a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Essential elements of ML\n",
     "\n",
@@ -561,9 +519,7 @@
   {
    "cell_type": "markdown",
    "id": "2b5e75d3",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## An optimization/minimization problem\n",
     "\n",
@@ -573,9 +529,7 @@
   {
    "cell_type": "markdown",
    "id": "68c077de",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## A Frequentist approach to data analysis\n",
     "\n",
@@ -608,9 +562,7 @@
   {
    "cell_type": "markdown",
    "id": "8eff8ace",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## What is a good model?\n",
     "\n",
@@ -638,9 +590,7 @@
   {
    "cell_type": "markdown",
    "id": "50d341fd",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## What is a good model? Can we define it?\n",
     "\n",
@@ -669,9 +619,7 @@
   {
    "cell_type": "markdown",
    "id": "267b6500",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Software and needed installations\n",
     "\n",
@@ -708,9 +656,7 @@
   {
    "cell_type": "markdown",
    "id": "0693b025",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Python installers\n",
     "\n",
@@ -741,9 +687,7 @@
   {
    "cell_type": "markdown",
    "id": "11750c35",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Useful Python libraries\n",
     "Here we list several useful Python libraries we strongly recommend (if you use anaconda many of these are already there)\n",
@@ -774,9 +718,7 @@
   {
    "cell_type": "markdown",
    "id": "437ff79d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Installing R, C++, cython or Julia\n",
     "\n",
@@ -798,9 +740,7 @@
   {
    "cell_type": "markdown",
    "id": "22cf44f9",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Installing R, C++, cython, Numba etc\n",
     "\n",
@@ -825,9 +765,7 @@
   {
    "cell_type": "markdown",
    "id": "092aa1f4",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "        pycod jupyter nbconvert filename.ipynb --to latex \n"
    ]
@@ -835,9 +773,7 @@
   {
    "cell_type": "markdown",
    "id": "5fb4d966",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "And to add more versatility, the Python package [SymPy](http://www.sympy.org/en/index.html) is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS)  and is entirely written in Python. \n",
     "\n",
@@ -849,9 +785,7 @@
   {
    "cell_type": "markdown",
    "id": "81967a6d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Numpy examples and Important Matrix and vector handling packages\n",
     "\n",
@@ -870,9 +804,7 @@
   {
    "cell_type": "markdown",
    "id": "f899fe44",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Basic Matrix Features\n",
     "\n",
@@ -882,9 +814,7 @@
   {
    "cell_type": "markdown",
    "id": "e7153ce7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\mathbf{A} =\n",
@@ -905,9 +835,7 @@
   {
    "cell_type": "markdown",
    "id": "039c9d2b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The inverse of a matrix is defined by"
    ]
@@ -915,9 +843,7 @@
   {
    "cell_type": "markdown",
    "id": "f5513bd7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\mathbf{A}^{-1} \\cdot \\mathbf{A} = I\n",
@@ -927,9 +853,7 @@
   {
    "cell_type": "markdown",
    "id": "15e6ca3c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "<table class=\"dotable\" border=\"1\">\n",
     "<thead>\n",
@@ -948,9 +872,7 @@
   {
    "cell_type": "markdown",
    "id": "e980e643",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Some famous Matrices\n",
     "\n",
@@ -976,9 +898,7 @@
   {
    "cell_type": "markdown",
    "id": "b76014b3",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### More Basic Matrix Features\n",
     "\n",
@@ -1002,9 +922,7 @@
   {
    "cell_type": "markdown",
    "id": "48a15a71",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Numpy and arrays\n",
     "[Numpy](http://www.numpy.org/) provides an easy way to handle arrays in Python. The standard way to import this library is as"
@@ -1014,10 +932,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "d355379c",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np"
@@ -1026,9 +941,7 @@
   {
    "cell_type": "markdown",
    "id": "875b0c57",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Here follows a simple example where we set up an array of ten elements, all determined by random numbers drawn according to the normal distribution,"
    ]
@@ -1037,10 +950,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "b8435832",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "n = 10\n",
@@ -1051,9 +961,7 @@
   {
    "cell_type": "markdown",
    "id": "aff17b91",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We defined a vector $x$ with $n=10$ elements with its values given by the Normal distribution $N(0,1)$.\n",
     "Another alternative is to declare a vector as follows"
@@ -1063,10 +971,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "1628226b",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1077,9 +982,7 @@
   {
    "cell_type": "markdown",
    "id": "d2504d4f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Here we have defined a vector with three elements, with $x_0=1$, $x_1=2$ and $x_2=3$. Note that both Python and C++\n",
     "start numbering array elements from $0$ and on. This means that a vector with $n$ elements has a sequence of entities $x_0, x_1, x_2, \\dots, x_{n-1}$. We could also let (recommended) Numpy to compute the logarithms of a specific array as"
@@ -1089,10 +992,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "3b227ba8",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1103,9 +1003,7 @@
   {
    "cell_type": "markdown",
    "id": "8e993172",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In the last example we used Numpy's unary function $np.log$. This function is\n",
     "highly tuned to compute array elements since the code is vectorized\n",
@@ -1120,10 +1018,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "49e5616c",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1137,9 +1032,7 @@
   {
    "cell_type": "markdown",
    "id": "8b7a130d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We note that our code is much longer already and we need to import the **log** function from the **math** module. \n",
     "The attentive reader will also notice that the output is $[1, 1, 2]$. Python interprets automagically our numbers as integers (like the **automatic** keyword in C++). To change this we could define our array elements to be double precision numbers as"
@@ -1149,10 +1042,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "d59a3734",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1163,9 +1053,7 @@
   {
    "cell_type": "markdown",
    "id": "7ccc9342",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "or simply write them as double precision numbers (Python uses 64 bits as default for floating point type variables), that is"
    ]
@@ -1174,10 +1062,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "1f0ef993",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1188,9 +1073,7 @@
   {
    "cell_type": "markdown",
    "id": "26ce093e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "To check the number of bytes (remember that one byte contains eight bits for double precision variables), you can use simple use the **itemsize** functionality (the array $x$ is actually an object which inherits the functionalities defined in Numpy) as"
    ]
@@ -1199,10 +1082,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "208f43ac",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1213,9 +1093,7 @@
   {
    "cell_type": "markdown",
    "id": "93ec757f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Matrices in Python\n",
     "\n",
@@ -1228,10 +1106,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "b8996f96",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1242,9 +1117,7 @@
   {
    "cell_type": "markdown",
    "id": "e0932c59",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "If we use the **shape** function we would get $(3, 3)$ as output, that is verifying that our matrix is a $3\\times 3$ matrix. We can slice the matrix and print for example the first column (Python organized matrix elements in a row-major order, see below) as"
    ]
@@ -1253,10 +1126,7 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "c6f27f9c",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1268,9 +1138,7 @@
   {
    "cell_type": "markdown",
    "id": "73723a49",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can continue this was by printing out other columns or rows. The example here prints out the second column"
    ]
@@ -1279,10 +1147,7 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "37d2fd90",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1294,9 +1159,7 @@
   {
    "cell_type": "markdown",
    "id": "05cc834e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Numpy contains many other functionalities that allow us to slice, subdivide etc etc arrays. We strongly recommend that you look up the [Numpy website for more details](http://www.numpy.org/). Useful functions when defining a matrix are the **np.zeros** function which declares a matrix of a given dimension and sets all elements to zero"
    ]
@@ -1305,10 +1168,7 @@
    "cell_type": "code",
    "execution_count": 12,
    "id": "e15a4fff",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1321,9 +1181,7 @@
   {
    "cell_type": "markdown",
    "id": "0b1ebc4d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "or initializing all elements to"
    ]
@@ -1332,10 +1190,7 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "f83b67d9",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1348,9 +1203,7 @@
   {
    "cell_type": "markdown",
    "id": "c28a9cd9",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "or as unitarily distributed random numbers (see the material on random number generators in the statistics part)"
    ]
@@ -1359,10 +1212,7 @@
    "cell_type": "code",
    "execution_count": 14,
    "id": "9aa190b7",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1375,9 +1225,7 @@
   {
    "cell_type": "markdown",
    "id": "3e3ab3a4",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "As we will see throughout these lectures, there are several extremely useful functionalities in Numpy.\n",
     "As an example, consider the discussion of the covariance matrix. Suppose we have defined three vectors\n",
@@ -1387,9 +1235,7 @@
   {
    "cell_type": "markdown",
    "id": "23bddd16",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{\\Sigma} = \\begin{bmatrix} \\sigma_{xx} & \\sigma_{xy} & \\sigma_{xz} \\\\\n",
@@ -1402,9 +1248,7 @@
   {
    "cell_type": "markdown",
    "id": "d256ad80",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where for example"
    ]
@@ -1412,9 +1256,7 @@
   {
    "cell_type": "markdown",
    "id": "fa974adf",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\sigma_{xy} =\\frac{1}{n} \\sum_{i=0}^{n-1}(x_i- \\overline{x})(y_i- \\overline{y}).\n",
@@ -1424,9 +1266,7 @@
   {
    "cell_type": "markdown",
    "id": "943d22f1",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The Numpy function **np.cov** calculates the covariance elements using the factor $1/(n-1)$ instead of $1/n$ since it assumes we do not have the exact mean values. \n",
     "The following simple function uses the **np.vstack** function which takes each vector of dimension $1\\times n$ and produces a $3\\times n$ matrix $\\boldsymbol{W}$"
@@ -1435,9 +1275,7 @@
   {
    "cell_type": "markdown",
    "id": "88370719",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{W} = \\begin{bmatrix} x_0 & x_1 & x_2 & \\dots & x_{n-2} & x_{n-1} \\\\\n",
@@ -1450,9 +1288,7 @@
   {
    "cell_type": "markdown",
    "id": "4cd9bb68",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "which in turn is converted into into the $3\\times 3$ covariance matrix\n",
     "$\\boldsymbol{\\Sigma}$ via the Numpy function **np.cov()**. We note that we can also calculate\n",
@@ -1465,10 +1301,7 @@
    "cell_type": "code",
    "execution_count": 15,
    "id": "89cfecda",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Importing various packages\n",
@@ -1492,10 +1325,7 @@
    "cell_type": "code",
    "execution_count": 16,
    "id": "1dc7eb64",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -1516,9 +1346,7 @@
   {
    "cell_type": "markdown",
    "id": "4c0484d8",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Meet the Pandas\n",
     "\n",
@@ -1542,10 +1370,7 @@
    "cell_type": "code",
    "execution_count": 17,
    "id": "bc5fb71a",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
@@ -1562,9 +1387,7 @@
   {
    "cell_type": "markdown",
    "id": "b7a91093",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In the above we have imported **pandas** with the shorthand **pd**, the latter has become the standard way we import **pandas**. We make then a list of various variables\n",
     "and reorganize the aboves lists into a **DataFrame** and then print out  a neat table with specific column labels as *Name*, *place of birth* and *date of birth*.\n",
@@ -1576,10 +1399,7 @@
    "cell_type": "code",
    "execution_count": 18,
    "id": "1f0d35dd",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])\n",
@@ -1589,9 +1409,7 @@
   {
    "cell_type": "markdown",
    "id": "e377afba",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Thereafter we display the content of the row which begins with the index **Aragorn**"
    ]
@@ -1600,10 +1418,7 @@
    "cell_type": "code",
    "execution_count": 19,
    "id": "01d60f31",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "display(data_pandas.loc['Aragorn'])"
@@ -1612,9 +1427,7 @@
   {
    "cell_type": "markdown",
    "id": "91710aa0",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can easily append data to this, for example"
    ]
@@ -1623,10 +1436,7 @@
    "cell_type": "code",
    "execution_count": 20,
    "id": "8fbef305",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "new_hobbit = {'First Name': [\"Peregrin\"],\n",
@@ -1641,9 +1451,7 @@
   {
    "cell_type": "markdown",
    "id": "a43653e3",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Here are other examples where we use the **DataFrame** functionality to handle arrays, now with more interesting features for us, namely numbers. We set up a matrix \n",
     "of dimensionality $10\\times 5$ and compute the mean value and standard deviation of each column. Similarly, we can perform mathematial operations like squaring the matrix elements and many other operations."
@@ -1653,10 +1461,7 @@
    "cell_type": "code",
    "execution_count": 21,
    "id": "23b64767",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1677,9 +1482,7 @@
   {
    "cell_type": "markdown",
    "id": "04086382",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Thereafter we can select specific columns only and plot final results"
    ]
@@ -1688,10 +1491,7 @@
    "cell_type": "code",
    "execution_count": 22,
    "id": "200065c9",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']\n",
@@ -1717,9 +1517,7 @@
   {
    "cell_type": "markdown",
    "id": "87c4af21",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can produce a $4\\times 4$ matrix"
    ]
@@ -1728,10 +1526,7 @@
    "cell_type": "code",
    "execution_count": 23,
    "id": "0f0e2ea4",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "b = np.arange(16).reshape((4,4))\n",
@@ -1743,9 +1538,7 @@
   {
    "cell_type": "markdown",
    "id": "7c8e41f2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and many other operations. \n",
     "\n",
@@ -1760,9 +1553,7 @@
   {
    "cell_type": "markdown",
    "id": "ff56b741",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Friday August 27\n",
     "\n",
@@ -1774,9 +1565,7 @@
   {
    "cell_type": "markdown",
    "id": "95f8b206",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Simple linear regression model using **scikit-learn**\n",
     "\n",
@@ -1805,9 +1594,7 @@
   {
    "cell_type": "markdown",
    "id": "15f8e533",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "y = 2x+N(0,1),\n",
@@ -1817,9 +1604,7 @@
   {
    "cell_type": "markdown",
    "id": "6def5844",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where $N(0,1)$ represents random numbers generated by the normal\n",
     "distribution.  From **Scikit-Learn** we import then the\n",
@@ -1845,10 +1630,7 @@
    "cell_type": "code",
    "execution_count": 24,
    "id": "3e056e1e",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Importing various packages\n",
@@ -1875,9 +1657,7 @@
   {
    "cell_type": "markdown",
    "id": "79d3bd41",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This example serves several aims. It allows us to demonstrate several\n",
     "aspects of data analysis and later machine learning algorithms. The\n",
@@ -1892,9 +1672,7 @@
   {
    "cell_type": "markdown",
    "id": "efe6d89d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "y = 10x+0.01 \\times N(0,1),\n",
@@ -1904,9 +1682,7 @@
   {
    "cell_type": "markdown",
    "id": "d66300c2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where $x$ is defined as before.  Does the fit look better? Indeed, by\n",
     "reducing the role of the noise given by the normal distribution we see immediately that\n",
@@ -1925,9 +1701,7 @@
   {
    "cell_type": "markdown",
    "id": "a73d3b9e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\chi^2 = \\frac{1}{n}\n",
@@ -1938,9 +1712,7 @@
   {
    "cell_type": "markdown",
    "id": "2cf633a8",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where $\\sigma_i^2$ is the variance (to be defined later) of the entry\n",
     "$y_i$.  We may not know the explicit value of $\\sigma_i^2$, it serves\n",
@@ -1969,9 +1741,7 @@
   {
    "cell_type": "markdown",
    "id": "97c9029a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\epsilon_{\\mathrm{relative}}= \\frac{\\vert \\boldsymbol{y} -\\boldsymbol{\\tilde{y}}\\vert}{\\vert \\boldsymbol{y}\\vert}.\n",
@@ -1981,9 +1751,7 @@
   {
    "cell_type": "markdown",
    "id": "84956e1c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The squared cost function results in an arithmetic mean-unbiased\n",
     "estimator, and the absolute-value cost function results in a\n",
@@ -1999,10 +1767,7 @@
    "cell_type": "code",
    "execution_count": 25,
    "id": "204b76db",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -2026,9 +1791,7 @@
   {
    "cell_type": "markdown",
    "id": "bb1efc74",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Depending on the parameter in front of the normal distribution, we may\n",
     "have a small or larger relative error. Try to play around with\n",
@@ -2048,10 +1811,7 @@
    "cell_type": "code",
    "execution_count": 26,
    "id": "90a40d1f",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np \n",
@@ -2086,9 +1846,7 @@
   {
    "cell_type": "markdown",
    "id": "1918b0a3",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The function **coef** gives us the parameter $\\beta$ of our fit while **intercept** yields \n",
     "$\\alpha$. Depending on the constant in front of the normal distribution, we get values near or far from $\\alpha =2$ and $\\beta =5$. Try to play around with different parameters in front of the normal distribution. The function **meansquarederror** gives us the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error or loss defined as"
@@ -2097,9 +1855,7 @@
   {
    "cell_type": "markdown",
    "id": "078f7289",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "MSE(\\boldsymbol{y},\\boldsymbol{\\tilde{y}}) = \\frac{1}{n}\n",
@@ -2110,9 +1866,7 @@
   {
    "cell_type": "markdown",
    "id": "23899ec9",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The smaller the value, the better the fit. Ideally we would like to\n",
     "have an MSE equal zero.  The attentive reader has probably recognized\n",
@@ -2131,9 +1885,7 @@
   {
    "cell_type": "markdown",
    "id": "881285ea",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "R^2(\\boldsymbol{y}, \\tilde{\\boldsymbol{y}}) = 1 - \\frac{\\sum_{i=0}^{n - 1} (y_i - \\tilde{y}_i)^2}{\\sum_{i=0}^{n - 1} (y_i - \\bar{y})^2},\n",
@@ -2143,9 +1895,7 @@
   {
    "cell_type": "markdown",
    "id": "ea902970",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where we have defined the mean value  of $\\boldsymbol{y}$ as"
    ]
@@ -2153,9 +1903,7 @@
   {
    "cell_type": "markdown",
    "id": "e60da213",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\bar{y} =  \\frac{1}{n} \\sum_{i=0}^{n - 1} y_i.\n",
@@ -2165,9 +1913,7 @@
   {
    "cell_type": "markdown",
    "id": "3f6be08b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Another quantity taht we will meet again in our discussions of regression analysis is \n",
     " the mean absolute error (MAE), a risk metric corresponding to the expected value of the absolute error loss or what we call the $l1$-norm loss. In our discussion above we presented the relative error.\n",
@@ -2177,9 +1923,7 @@
   {
    "cell_type": "markdown",
    "id": "440c7b62",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\text{MAE}(\\boldsymbol{y}, \\boldsymbol{\\tilde{y}}) = \\frac{1}{n} \\sum_{i=0}^{n-1} \\left| y_i - \\tilde{y}_i \\right|.\n",
@@ -2189,9 +1933,7 @@
   {
    "cell_type": "markdown",
    "id": "b05a3f98",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We present the \n",
     "squared logarithmic (quadratic) error"
@@ -2200,9 +1942,7 @@
   {
    "cell_type": "markdown",
    "id": "4cd33173",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\text{MSLE}(\\boldsymbol{y}, \\boldsymbol{\\tilde{y}}) = \\frac{1}{n} \\sum_{i=0}^{n - 1} (\\log_e (1 + y_i) - \\log_e (1 + \\tilde{y}_i) )^2,\n",
@@ -2212,9 +1952,7 @@
   {
    "cell_type": "markdown",
    "id": "77f5461f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where $\\log_e (x)$ stands for the natural logarithm of $x$. This error\n",
     "estimate is best to use when targets having exponential growth, such\n",
@@ -2234,9 +1972,7 @@
   {
    "cell_type": "markdown",
    "id": "96e7b86e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "H_{\\delta}(\\boldsymbol{a})=\\left\\{\\begin{array}{cc}\\frac{1}{2} \\boldsymbol{a}^{2}& \\text{for }|\\boldsymbol{a}|\\leq \\delta\\\\ \\delta (|\\boldsymbol{a}|-\\frac{1}{2}\\delta ),&\\text{otherwise}.\\end{array}\\right.\n",
@@ -2246,9 +1982,7 @@
   {
    "cell_type": "markdown",
    "id": "a91d5d7b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Here $\\boldsymbol{a}=\\boldsymbol{y} - \\boldsymbol{\\tilde{y}}$.\n",
     "\n",
@@ -2262,10 +1996,7 @@
    "cell_type": "code",
    "execution_count": 27,
    "id": "cc7c950e",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -2303,9 +2034,7 @@
   {
    "cell_type": "markdown",
    "id": "dc81b198",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### To our real data: nuclear binding energies. Brief reminder on masses and binding energies\n",
     "\n",
@@ -2320,9 +2049,7 @@
   {
    "cell_type": "markdown",
    "id": "c647a2bb",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\Delta M(N, Z) =  M(N, Z) - uA,\n",
@@ -2332,9 +2059,7 @@
   {
    "cell_type": "markdown",
    "id": "6bf735eb",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where $u$ is the Atomic Mass Unit"
    ]
@@ -2342,9 +2067,7 @@
   {
    "cell_type": "markdown",
    "id": "21bc983d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "u = M(^{12}\\mathrm{C})/12 = 931.4940954(57) \\hspace{0.1cm} \\mathrm{MeV}/c^2.\n",
@@ -2354,9 +2077,7 @@
   {
    "cell_type": "markdown",
    "id": "29f11904",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The nucleon masses are"
    ]
@@ -2364,9 +2085,7 @@
   {
    "cell_type": "markdown",
    "id": "7066ba82",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "m_p =  1.00727646693(9)u,\n",
@@ -2376,9 +2095,7 @@
   {
    "cell_type": "markdown",
    "id": "b6551094",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and"
    ]
@@ -2386,9 +2103,7 @@
   {
    "cell_type": "markdown",
    "id": "d7e2c32e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "m_n = 939.56536(8)\\hspace{0.1cm} \\mathrm{MeV}/c^2 = 1.0086649156(6)u.\n",
@@ -2398,9 +2113,7 @@
   {
    "cell_type": "markdown",
    "id": "a2b7b470",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In the [2016 mass evaluation of by W.J.Huang, G.Audi, M.Wang, F.G.Kondev, S.Naimi and X.Xu](http://nuclearmasses.org/resources_folder/Wang_2017_Chinese_Phys_C_41_030003.pdf)\n",
     "there are data on masses and decays of 3437 nuclei.\n",
@@ -2414,9 +2127,7 @@
   {
    "cell_type": "markdown",
    "id": "682b0bd1",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "BE(N, Z) = ZM_H c^2 + Nm_n c^2 - M(N, Z)c^2 ,\n",
@@ -2426,9 +2137,7 @@
   {
    "cell_type": "markdown",
    "id": "f8b5e709",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where $M_H$ is the mass of the hydrogen atom and $m_n$ is the mass of the neutron.\n",
     "In terms of the mass excess the binding energy is given by"
@@ -2437,9 +2146,7 @@
   {
    "cell_type": "markdown",
    "id": "3da0afe2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "BE(N, Z) = Z\\Delta_H c^2 + N\\Delta_n c^2 -\\Delta(N, Z)c^2 ,\n",
@@ -2449,9 +2156,7 @@
   {
    "cell_type": "markdown",
    "id": "b241cf42",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where $\\Delta_H c^2 = 7.2890$ MeV and $\\Delta_n c^2 = 8.0713$ MeV.\n",
     "\n",
@@ -2463,9 +2168,7 @@
   {
    "cell_type": "markdown",
    "id": "0f78c0ba",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "BE(N,Z) = a_1A-a_2A^{2/3}-a_3\\frac{Z^2}{A^{1/3}}-a_4\\frac{(N-Z)^2}{A},\n",
@@ -2475,9 +2178,7 @@
   {
    "cell_type": "markdown",
    "id": "f63cba5f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where $A$ stands for the number of nucleons and the $a_i$s are parameters which are determined by a fit \n",
     "to the experimental data.  \n",
@@ -2500,9 +2201,7 @@
   {
    "cell_type": "markdown",
    "id": "2284d6ec",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Organizing our data\n",
     "\n",
@@ -2515,12 +2214,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 10,
    "id": "bd7aa620",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Common imports\n",
@@ -2561,21 +2257,16 @@
   {
    "cell_type": "markdown",
    "id": "df2c8ccf",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Before we proceed, we define also a function for making our plots. You can obviously avoid this and simply set up various **matplotlib** commands every time you need them. You may however find it convenient to collect all such commands in one function and simply call this function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 11,
    "id": "cb52c00e",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from pylab import plt, mpl\n",
@@ -2594,9 +2285,7 @@
   {
    "cell_type": "markdown",
    "id": "38545028",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Our next step is to read the data on experimental binding energies and\n",
     "reorganize them as functions of the mass number $A$, the number of\n",
@@ -2609,13 +2298,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 12,
    "id": "079dae1b",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'                                                                                                                         \\nThis is taken from the data file of the mass 2016 evaluation.                                                               \\nAll files are 3436 lines long with 124 character per line.                                                                  \\n       Headers are 39 lines long.                                                                                           \\n   col 1     :  Fortran character control: 1 = page feed  0 = line feed                                                     \\n   format    :  a1,i3,i5,i5,i5,1x,a3,a4,1x,f13.5,f11.5,f11.3,f9.3,1x,a2,f11.3,f9.3,1x,i3,1x,f12.5,f11.5                     \\n   These formats are reflected in the pandas widths variable below, see the statement                                       \\n   widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),                                                            \\n   Pandas has also a variable header, with length 39 in this case.                                                          \\n'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"                                                                                                                         \n",
     "This is taken from the data file of the mass 2016 evaluation.                                                               \n",
@@ -2632,9 +2329,7 @@
   {
    "cell_type": "markdown",
    "id": "eb12107a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The data we are interested in are in columns 2, 3, 4 and 11, giving us\n",
     "the number of neutrons, protons, mass numbers and binding energies,\n",
@@ -2644,12 +2339,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 13,
    "id": "206b12f1",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Read the experimental data with Pandas\n",
@@ -2675,9 +2367,7 @@
   {
    "cell_type": "markdown",
    "id": "c79ac328",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We have now read in the data, grouped them according to the variables we are interested in. \n",
     "We see how easy it is to reorganize the data using **pandas**. If we\n",
@@ -2693,13 +2383,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 14,
    "id": "5e6b60e8",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            N    Z    A Element  Ebinding\n",
+      "A                                        \n",
+      "4   0       1    3    4      Li  1.153760\n",
+      "5   2       3    2    5      He  5.512132\n",
+      "6   7       3    3    6      Li  5.332331\n",
+      "7   12      4    3    7      Li  5.606439\n",
+      "8   17      4    4    8      Be  7.062435\n",
+      "...       ...  ...  ...     ...       ...\n",
+      "264 3297  156  108  264      Hs  7.298375\n",
+      "265 3303  157  108  265      Hs  7.296247\n",
+      "266 3310  158  108  266      Hs  7.298273\n",
+      "269 3331  159  110  269      Ds  7.250154\n",
+      "270 3337  160  110  270      Ds  7.253775\n",
+      "\n",
+      "[264 rows x 5 columns]\n"
+     ]
+    }
+   ],
    "source": [
     "A = Masses['A']\n",
     "Z = Masses['Z']\n",
@@ -2712,9 +2421,7 @@
   {
    "cell_type": "markdown",
    "id": "d35730c6",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The next step, and we will define this mathematically later, is to set up the so-called **design matrix**. We will throughout call this matrix $\\boldsymbol{X}$.\n",
     "It has dimensionality $p\\times n$, where $n$ is the number of data points and $p$ are the so-called predictors. In our case here they are given by the number of polynomials in $A$ we wish to include in the fit."
@@ -2722,12 +2429,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 15,
    "id": "2975893e",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Now we set up the design matrix X\n",
@@ -2742,21 +2446,16 @@
   {
    "cell_type": "markdown",
    "id": "3160f4c5",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "With **scikitlearn** we are now ready to use linear regression and fit our data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 16,
    "id": "dc828ff8",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "clf = skl.LinearRegression().fit(X, Energies)\n",
@@ -2766,9 +2465,7 @@
   {
    "cell_type": "markdown",
    "id": "3744b3e4",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Pretty simple!  \n",
     "Now we can print measures of how our fit is doing, the coefficients from the fits and plot the final fit together with our data."
@@ -2776,13 +2473,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 17,
    "id": "ac4670de",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean squared error: 0.02\n",
+      "Variance score: 0.95\n",
+      "Mean absolute error: 0.05\n",
+      "[ 0.00000000e+00 -2.96611194e-02  2.01719003e-01  1.08078025e+01\n",
+      " -4.03097597e+01] 5.294399745619598\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# The mean squared error                               \n",
     "print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, fity))\n",
@@ -2809,9 +2525,7 @@
   {
    "cell_type": "markdown",
    "id": "7da29fb8",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Seeing the wood for the trees\n",
     "\n",
@@ -2820,20 +2534,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 20,
    "id": "2c8f264b",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            N    Z    A Element  Ebinding   Eapprox\n",
+      "A                                                  \n",
+      "4   0       1    3    4      Li  1.153760  1.153760\n",
+      "5   2       3    2    5      He  5.512132  5.422231\n",
+      "6   7       3    3    6      Li  5.332331  5.422231\n",
+      "7   12      4    3    7      Li  5.606439  5.606439\n",
+      "8   17      4    4    8      Be  7.062435  7.382774\n",
+      "...       ...  ...  ...     ...       ...       ...\n",
+      "264 3297  156  108  264      Hs  7.298375  7.512041\n",
+      "265 3303  157  108  265      Hs  7.296247  7.512041\n",
+      "266 3310  158  108  266      Hs  7.298273  7.512041\n",
+      "269 3331  159  110  269      Ds  7.250154  7.512041\n",
+      "270 3337  160  110  270      Ds  7.253775  7.512041\n",
+      "\n",
+      "[264 rows x 6 columns]\n",
+      "0.007012403613997257\n"
+     ]
+    }
+   ],
    "source": [
     "\n",
     "#Decision Tree Regression\n",
     "from sklearn.tree import DecisionTreeRegressor\n",
     "regr_1=DecisionTreeRegressor(max_depth=5)\n",
     "regr_2=DecisionTreeRegressor(max_depth=7)\n",
-    "regr_3=DecisionTreeRegressor(max_depth=9)\n",
+    "regr_3=DecisionTreeRegressor(max_depth=3)\n",
     "regr_1.fit(X, Energies)\n",
     "regr_2.fit(X, Energies)\n",
     "regr_3.fit(X, Energies)\n",
@@ -2846,8 +2590,8 @@
     "# Plot the results\n",
     "plt.figure()\n",
     "plt.plot(A, Energies, color=\"blue\", label=\"Data\", linewidth=2)\n",
-    "plt.plot(A, y_1, color=\"red\", label=\"max_depth=5\", linewidth=2)\n",
-    "plt.plot(A, y_2, color=\"green\", label=\"max_depth=7\", linewidth=2)\n",
+    "#plt.plot(A, y_1, color=\"red\", label=\"max_depth=5\", linewidth=2)\n",
+    "#plt.plot(A, y_2, color=\"green\", label=\"max_depth=7\", linewidth=2)\n",
     "plt.plot(A, y_3, color=\"m\", label=\"max_depth=9\", linewidth=2)\n",
     "\n",
     "plt.xlabel(\"$A$\")\n",
@@ -2863,9 +2607,7 @@
   {
    "cell_type": "markdown",
    "id": "5899eaad",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### And what about using neural networks?\n",
     "\n",
@@ -2877,10 +2619,7 @@
    "cell_type": "code",
    "execution_count": 37,
    "id": "0639aaf3",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.neural_network import MLPRegressor\n",
@@ -2917,9 +2656,7 @@
   {
    "cell_type": "markdown",
    "id": "bed3130b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## A first summary\n",
     "\n",
@@ -2937,9 +2674,7 @@
   {
    "cell_type": "markdown",
    "id": "d89a7826",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Why Linear Regression (aka Ordinary Least Squares and family)\n",
     "\n",
@@ -2969,9 +2704,7 @@
   {
    "cell_type": "markdown",
    "id": "481c6cb2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Regression analysis, overarching aims\n",
     "\n",
@@ -2991,9 +2724,7 @@
   {
    "cell_type": "markdown",
    "id": "f1c09a01",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Regression analysis, overarching aims II\n",
     "\n",
@@ -3019,9 +2750,7 @@
   {
    "cell_type": "markdown",
    "id": "866b4aa9",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Examples\n",
     "In order to understand the relation among the predictors $p$, the set of data $n$ and the target (outcome, output etc) $\\boldsymbol{y}$,\n",
@@ -3034,9 +2763,7 @@
   {
    "cell_type": "markdown",
    "id": "a3ae9da5",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "BE(A) = a_0+a_1A+a_2A^{2/3}+a_3A^{-1/3}+a_4A^{-1},\n",
@@ -3046,9 +2773,7 @@
   {
    "cell_type": "markdown",
    "id": "672c981f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "we have five predictors, that is the intercept, the $A$ dependent term, the $A^{2/3}$ term and the $A^{-1/3}$ and $A^{-1}$ terms.\n",
     "This gives $p=0,1,2,3,4$. Furthermore we have $n$ entries for each predictor. It means that our design matrix is a \n",
@@ -3061,9 +2786,7 @@
   {
    "cell_type": "markdown",
    "id": "6081f608",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## General linear models\n",
     "Before we proceed let us study a case from linear algebra where we aim at fitting a set of data $\\boldsymbol{y}=[y_0,y_1,\\dots,y_{n-1}]$. We could think of these data as a result of an experiment or a complicated numerical experiment. These data are functions of a series of variables $\\boldsymbol{x}=[x_0,x_1,\\dots,x_{n-1}]$, that is $y_i = y(x_i)$ with $i=0,1,2,\\dots,n-1$. The variables $x_i$ could represent physical quantities like time, temperature, position etc. We assume that $y(x)$ is a smooth function. \n",
@@ -3074,9 +2797,7 @@
   {
    "cell_type": "markdown",
    "id": "142a005f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "y=y(x) \\rightarrow y(x_i)=\\tilde{y}_i+\\epsilon_i=\\sum_{j=0}^{n-1} \\beta_j x_i^j+\\epsilon_i,\n",
@@ -3086,9 +2807,7 @@
   {
    "cell_type": "markdown",
    "id": "49446a2f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where $\\epsilon_i$ is the error in our approximation."
    ]
@@ -3096,9 +2815,7 @@
   {
    "cell_type": "markdown",
    "id": "72044f3f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Rewriting the fitting procedure as a linear algebra problem\n",
     "For every set of values $y_i,x_i$ we have thus the corresponding set of equations"
@@ -3107,9 +2824,7 @@
   {
    "cell_type": "markdown",
    "id": "5978a866",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\begin{align*}\n",
@@ -3125,9 +2840,7 @@
   {
    "cell_type": "markdown",
    "id": "b13072e8",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Rewriting the fitting procedure as a linear algebra problem, more details\n",
     "Defining the vectors"
@@ -3136,9 +2849,7 @@
   {
    "cell_type": "markdown",
    "id": "6056f36e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{y} = [y_0,y_1, y_2,\\dots, y_{n-1}]^T,\n",
@@ -3148,9 +2859,7 @@
   {
    "cell_type": "markdown",
    "id": "34f71f52",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and"
    ]
@@ -3158,9 +2867,7 @@
   {
    "cell_type": "markdown",
    "id": "2edeb755",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{\\beta} = [\\beta_0,\\beta_1, \\beta_2,\\dots, \\beta_{n-1}]^T,\n",
@@ -3170,9 +2877,7 @@
   {
    "cell_type": "markdown",
    "id": "e004a257",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and"
    ]
@@ -3180,9 +2885,7 @@
   {
    "cell_type": "markdown",
    "id": "636097f2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{\\epsilon} = [\\epsilon_0,\\epsilon_1, \\epsilon_2,\\dots, \\epsilon_{n-1}]^T,\n",
@@ -3192,9 +2895,7 @@
   {
    "cell_type": "markdown",
    "id": "8577dbb6",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and the design matrix"
    ]
@@ -3202,9 +2903,7 @@
   {
    "cell_type": "markdown",
    "id": "e76f9a38",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{X}=\n",
@@ -3221,9 +2920,7 @@
   {
    "cell_type": "markdown",
    "id": "aa89a70c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "we can rewrite our equations as"
    ]
@@ -3231,9 +2928,7 @@
   {
    "cell_type": "markdown",
    "id": "c6ca5c9b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta}+\\boldsymbol{\\epsilon}.\n",
@@ -3243,9 +2938,7 @@
   {
    "cell_type": "markdown",
    "id": "b657c2e4",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The above design matrix is called a [Vandermonde matrix](https://en.wikipedia.org/wiki/Vandermonde_matrix)."
    ]
@@ -3253,9 +2946,7 @@
   {
    "cell_type": "markdown",
    "id": "965e8d48",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Generalizing the fitting procedure as a linear algebra problem\n",
     "\n",
@@ -3269,9 +2960,7 @@
   {
    "cell_type": "markdown",
    "id": "ef647144",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\begin{align*}\n",
@@ -3289,9 +2978,7 @@
   {
    "cell_type": "markdown",
    "id": "e562da77",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "**Note that we have $p=n$ here. The matrix is symmetric. This is generally not the case!**"
    ]
@@ -3299,9 +2986,7 @@
   {
    "cell_type": "markdown",
    "id": "e8ba4531",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Generalizing the fitting procedure as a linear algebra problem\n",
     "We redefine in turn the matrix $\\boldsymbol{X}$ as"
@@ -3310,9 +2995,7 @@
   {
    "cell_type": "markdown",
    "id": "528b1a26",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{X}=\n",
@@ -3329,9 +3012,7 @@
   {
    "cell_type": "markdown",
    "id": "fd83859b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and without loss of generality we rewrite again  our equations as"
    ]
@@ -3339,9 +3020,7 @@
   {
    "cell_type": "markdown",
    "id": "a31fc391",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta}+\\boldsymbol{\\epsilon}.\n",
@@ -3351,9 +3030,7 @@
   {
    "cell_type": "markdown",
    "id": "637b3c41",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The left-hand side of this equation is kwown. Our error vector $\\boldsymbol{\\epsilon}$ and the parameter vector $\\boldsymbol{\\beta}$ are our unknow quantities. How can we obtain the optimal set of $\\beta_i$ values?"
    ]
@@ -3361,9 +3038,7 @@
   {
    "cell_type": "markdown",
    "id": "a449ee2e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Optimizing our parameters\n",
     "We have defined the matrix $\\boldsymbol{X}$ via the equations"
@@ -3372,9 +3047,7 @@
   {
    "cell_type": "markdown",
    "id": "2f5357e7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\begin{align*}\n",
@@ -3392,9 +3065,7 @@
   {
    "cell_type": "markdown",
    "id": "8618a946",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "As we noted above, we stayed with a system with the design matrix \n",
     " $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times n}$, that is we have $p=n$. For reasons to come later (algorithmic arguments) we will hereafter define \n",
@@ -3404,9 +3075,7 @@
   {
    "cell_type": "markdown",
    "id": "e17e4fbf",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Our model for the nuclear binding energies\n",
     "\n",
@@ -3417,13 +3086,162 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 1,
    "id": "585d5f0d",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1</th>\n",
+       "      <th>A</th>\n",
+       "      <th>A^(2/3)</th>\n",
+       "      <th>A^(-1/3)</th>\n",
+       "      <th>1/A</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.519842</td>\n",
+       "      <td>0.629961</td>\n",
+       "      <td>0.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.924018</td>\n",
+       "      <td>0.584804</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3.301927</td>\n",
+       "      <td>0.550321</td>\n",
+       "      <td>0.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>3.659306</td>\n",
+       "      <td>0.522758</td>\n",
+       "      <td>0.142857</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.125000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>264.0</td>\n",
+       "      <td>41.153106</td>\n",
+       "      <td>0.155883</td>\n",
+       "      <td>0.003788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>265.0</td>\n",
+       "      <td>41.256962</td>\n",
+       "      <td>0.155687</td>\n",
+       "      <td>0.003774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>266.0</td>\n",
+       "      <td>41.360688</td>\n",
+       "      <td>0.155491</td>\n",
+       "      <td>0.003759</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>269</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>269.0</td>\n",
+       "      <td>41.671089</td>\n",
+       "      <td>0.154911</td>\n",
+       "      <td>0.003717</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>270</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>41.774300</td>\n",
+       "      <td>0.154720</td>\n",
+       "      <td>0.003704</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>264 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       1      A    A^(2/3)  A^(-1/3)       1/A\n",
+       "A                                             \n",
+       "4    1.0    4.0   2.519842  0.629961  0.250000\n",
+       "5    1.0    5.0   2.924018  0.584804  0.200000\n",
+       "6    1.0    6.0   3.301927  0.550321  0.166667\n",
+       "7    1.0    7.0   3.659306  0.522758  0.142857\n",
+       "8    1.0    8.0   4.000000  0.500000  0.125000\n",
+       "..   ...    ...        ...       ...       ...\n",
+       "264  1.0  264.0  41.153106  0.155883  0.003788\n",
+       "265  1.0  265.0  41.256962  0.155687  0.003774\n",
+       "266  1.0  266.0  41.360688  0.155491  0.003759\n",
+       "269  1.0  269.0  41.671089  0.154911  0.003717\n",
+       "270  1.0  270.0  41.774300  0.154720  0.003704\n",
+       "\n",
+       "[264 rows x 5 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Common imports\n",
     "import numpy as np\n",
@@ -3499,9 +3317,7 @@
   {
    "cell_type": "markdown",
    "id": "c4e97b66",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "With $\\boldsymbol{\\beta}\\in {\\mathbb{R}}^{p\\times 1}$, it means that we will hereafter write our equations for the approximation as"
    ]
@@ -3509,9 +3325,7 @@
   {
    "cell_type": "markdown",
    "id": "851561be",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{\\tilde{y}}= \\boldsymbol{X}\\boldsymbol{\\beta},\n",
@@ -3521,9 +3335,7 @@
   {
    "cell_type": "markdown",
    "id": "7bbe502f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "throughout these lectures."
    ]
@@ -3531,9 +3343,7 @@
   {
    "cell_type": "markdown",
    "id": "320412db",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Optimizing our parameters, more details\n",
     "With the above we use the design matrix to define the approximation $\\boldsymbol{\\tilde{y}}$ via the unknown quantity $\\boldsymbol{\\beta}$ as"
@@ -3542,9 +3352,7 @@
   {
    "cell_type": "markdown",
    "id": "267dc222",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{\\tilde{y}}= \\boldsymbol{X}\\boldsymbol{\\beta},\n",
@@ -3554,9 +3362,7 @@
   {
    "cell_type": "markdown",
    "id": "96837d16",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and in order to find the optimal parameters $\\beta_i$ instead of solving the above linear algebra problem, we define a function which gives a measure of the spread between the values $y_i$ (which represent hopefully the exact values) and the parameterized values $\\tilde{y}_i$, namely"
    ]
@@ -3564,9 +3370,7 @@
   {
    "cell_type": "markdown",
    "id": "d14d60ef",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "C(\\boldsymbol{\\beta})=\\frac{1}{n}\\sum_{i=0}^{n-1}\\left(y_i-\\tilde{y}_i\\right)^2=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)\\right\\},\n",
@@ -3576,9 +3380,7 @@
   {
    "cell_type": "markdown",
    "id": "0cddd579",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "or using the matrix $\\boldsymbol{X}$ and in a more compact matrix-vector notation as"
    ]
@@ -3586,9 +3388,7 @@
   {
    "cell_type": "markdown",
    "id": "93cd22d5",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "C(\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\}.\n",
@@ -3598,9 +3398,7 @@
   {
    "cell_type": "markdown",
    "id": "86057c7e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This function is one possible way to define the so-called cost function.\n",
     "\n",
@@ -3611,9 +3409,7 @@
   {
    "cell_type": "markdown",
    "id": "71f40902",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "C(\\boldsymbol{\\beta})=\\frac{1}{2n}\\sum_{i=0}^{n-1}\\left(y_i-\\tilde{y}_i\\right)^2,\n",
@@ -3623,9 +3419,7 @@
   {
    "cell_type": "markdown",
    "id": "6c65d7ce",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "since when taking the first derivative with respect to the unknown parameters $\\beta$, the factor of $2$ cancels out."
    ]
@@ -3633,9 +3427,7 @@
   {
    "cell_type": "markdown",
    "id": "9ac4402d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Interpretations and optimizing our parameters\n",
     "\n",
@@ -3645,9 +3437,7 @@
   {
    "cell_type": "markdown",
    "id": "64dc60d0",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "C(\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\},\n",
@@ -3657,9 +3447,7 @@
   {
    "cell_type": "markdown",
    "id": "03d22178",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "can be linked to the variance of the quantity $y_i$ if we interpret the latter as the mean value. \n",
     "When linking (see the discussion below) with the maximum likelihood approach below, we will indeed interpret $y_i$ as a mean value"
@@ -3668,9 +3456,7 @@
   {
    "cell_type": "markdown",
    "id": "e4aa5262",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "y_{i}=\\langle y_i \\rangle = \\beta_0x_{i,0}+\\beta_1x_{i,1}+\\beta_2x_{i,2}+\\dots+\\beta_{n-1}x_{i,n-1}+\\epsilon_i,\n",
@@ -3680,9 +3466,7 @@
   {
    "cell_type": "markdown",
    "id": "a19372cf",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where $\\langle y_i \\rangle$ is the mean value. Keep in mind also that\n",
     "till now we have treated $y_i$ as the exact value. Normally, the\n",
@@ -3699,9 +3483,7 @@
   {
    "cell_type": "markdown",
    "id": "e5b3f29c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in\n",
@@ -3712,9 +3494,7 @@
   {
    "cell_type": "markdown",
    "id": "34073125",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In practical terms it means we will require"
    ]
@@ -3722,9 +3502,7 @@
   {
    "cell_type": "markdown",
    "id": "26df781a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\beta_j} = \\frac{\\partial }{\\partial \\beta_j}\\left[ \\frac{1}{n}\\sum_{i=0}^{n-1}\\left(y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}\\right)^2\\right]=0,\n",
@@ -3734,9 +3512,7 @@
   {
    "cell_type": "markdown",
    "id": "3b0b5526",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "which results in"
    ]
@@ -3744,9 +3520,7 @@
   {
    "cell_type": "markdown",
    "id": "cf86a5d1",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\beta_j} = -\\frac{2}{n}\\left[ \\sum_{i=0}^{n-1}x_{ij}\\left(y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}\\right)\\right]=0,\n",
@@ -3756,9 +3530,7 @@
   {
    "cell_type": "markdown",
    "id": "4a6856ec",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "or in a matrix-vector form as"
    ]
@@ -3766,9 +3538,7 @@
   {
    "cell_type": "markdown",
    "id": "dd2f08f5",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right).\n",
@@ -3778,9 +3548,7 @@
   {
    "cell_type": "markdown",
    "id": "2a4d9c03",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Interpretations and optimizing our parameters\n",
     "We can rewrite"
@@ -3789,9 +3557,7 @@
   {
    "cell_type": "markdown",
    "id": "5bff7479",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right),\n",
@@ -3801,9 +3567,7 @@
   {
    "cell_type": "markdown",
    "id": "fbbc7423",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "as"
    ]
@@ -3811,9 +3575,7 @@
   {
    "cell_type": "markdown",
    "id": "23f8ea39",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{X}^T\\boldsymbol{y} = \\boldsymbol{X}^T\\boldsymbol{X}\\boldsymbol{\\beta},\n",
@@ -3823,9 +3585,7 @@
   {
    "cell_type": "markdown",
    "id": "f4651640",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and if the matrix $\\boldsymbol{X}^T\\boldsymbol{X}$ is invertible we have the solution"
    ]
@@ -3833,9 +3593,7 @@
   {
    "cell_type": "markdown",
    "id": "d378840b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{\\beta} =\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n",
@@ -3845,9 +3603,7 @@
   {
    "cell_type": "markdown",
    "id": "2fd84dbc",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We note also that since our design matrix is defined as $\\boldsymbol{X}\\in\n",
     "{\\mathbb{R}}^{n\\times p}$, the product $\\boldsymbol{X}^T\\boldsymbol{X} \\in\n",
@@ -3866,9 +3622,7 @@
   {
    "cell_type": "markdown",
    "id": "8b80441c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Some useful matrix and vector expressions\n",
     "\n",
@@ -3879,9 +3633,7 @@
   {
    "cell_type": "markdown",
    "id": "c982c326",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial (\\boldsymbol{b}^T\\boldsymbol{a})}{\\partial \\boldsymbol{a}} = \\boldsymbol{b},\n",
@@ -3891,9 +3643,7 @@
   {
    "cell_type": "markdown",
    "id": "e9cea18f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial (\\boldsymbol{a}^T\\boldsymbol{A}\\boldsymbol{a})}{\\partial \\boldsymbol{a}} = (\\boldsymbol{A}+\\boldsymbol{A}^T)\\boldsymbol{a},\n",
@@ -3903,9 +3653,7 @@
   {
    "cell_type": "markdown",
    "id": "65c68c56",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial tr(\\boldsymbol{B}\\boldsymbol{A})}{\\partial \\boldsymbol{A}} = \\boldsymbol{B}^T,\n",
@@ -3915,9 +3663,7 @@
   {
    "cell_type": "markdown",
    "id": "9551f6f2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial \\log{\\vert\\boldsymbol{A}\\vert}}{\\partial \\boldsymbol{A}} = (\\boldsymbol{A}^{-1})^T.\n",
@@ -3927,9 +3673,7 @@
   {
    "cell_type": "markdown",
    "id": "99d05458",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Interpretations and optimizing our parameters\n",
     "The residuals $\\boldsymbol{\\epsilon}$ are in turn given by"
@@ -3938,9 +3682,7 @@
   {
    "cell_type": "markdown",
    "id": "1be9ba42",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{\\epsilon} = \\boldsymbol{y}-\\boldsymbol{\\tilde{y}} = \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta},\n",
@@ -3950,9 +3692,7 @@
   {
    "cell_type": "markdown",
    "id": "8e7bd501",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and with"
    ]
@@ -3960,9 +3700,7 @@
   {
    "cell_type": "markdown",
    "id": "84f6349f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)= 0,\n",
@@ -3972,9 +3710,7 @@
   {
    "cell_type": "markdown",
    "id": "2d1ae204",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "we have"
    ]
@@ -3982,9 +3718,7 @@
   {
    "cell_type": "markdown",
    "id": "c345bc62",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{X}^T\\boldsymbol{\\epsilon}=\\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)= 0,\n",
@@ -3994,9 +3728,7 @@
   {
    "cell_type": "markdown",
    "id": "029df1ed",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "meaning that the solution for $\\boldsymbol{\\beta}$ is the one which minimizes the residuals.  Later we will link this with the maximum likelihood approach.\n",
     "\n",
@@ -4006,9 +3738,7 @@
   {
    "cell_type": "markdown",
    "id": "5245bb29",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Own code for Ordinary Least Squares\n",
     "\n",
@@ -4018,12 +3748,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 2,
    "id": "76e7e216",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# matrix inversion to find beta\n",
@@ -4035,21 +3762,16 @@
   {
    "cell_type": "markdown",
    "id": "2da324cf",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Alternatively, you can use the least squares functionality in **Numpy** as"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 3,
    "id": "6a508b28",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fit = np.linalg.lstsq(X, Energies, rcond =None)[0]\n",
@@ -4059,22 +3781,30 @@
   {
    "cell_type": "markdown",
    "id": "c998781e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "And finally we plot our fit with and compare with data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 4,
    "id": "17ad0305",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEICAYAAABcVE8dAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAujklEQVR4nO3deZhc9X3n+/f3nNqr90VSS61dAiQhIUBgNoMdJyy2MQmJ/RAmvrZvGG7GzgzxJLnXmXkyjj3JPPY8vvGaxMMdMo4dI0+84C2GYMwSYxuBBBJCC0JC3VJr7b27uvY63/vHqW61hJZeq7qrvq/nqadLVdXn/H5U86lffc85v5+oKsYYYyqfU+4GGGOMKQ0LfGOMqRIW+MYYUyUs8I0xpkpY4BtjTJUIlLsBF9LS0qIrVqwodzOMMWZe2bFjR4+qtp7vuTkb+CtWrGD79u3lboYxxswrItJ5oedKWtIRkYdE5DUR2SMif1TKfRtjTLUrWeCLyJXAvwWuB64C3isia0q1f2OMqXalHOGvA7apalJV88BzwL0l3L8xxlS1Ugb+a8DbRaRZRGLAu4Gl418gIg+KyHYR2d7d3V3CphljTOUrWeCr6j7gs8CTwBPATqBwzmseVtUtqrqltfW8B5mNMcZMUUkP2qrqI6p6rareCvQDB0q5f2OMqWYlPS1TRBao6mkRWYZfv7+hlPs3xphqVurz8L8rIs1ADviYqg6UeP/zWsFTOnpHWNIQpas/STjgcuDUMIlMnvpokIOnE7xtZTPpfIHGWIhfHeohHHC5eW0L6VyBVS1xRKTc3TDGlElJA19V317K/VWKfz3Qze5jgwylchw8nUAELrSMwa8O9b7lsX/ZcxKAxniIeMhlY3sD3cMZljZF+fV1C3EdIeAI2YJHOOBOu73pXIGg6+A69uFizFwyZ6+0rXbpXIG/e/YQA8ksXf2pscdDAYds3qO5JkS+4NEQDLI4FmFwOMviWJgDXcM0RYOcHErTVhcllS3Qk8gQdIXh02n6XHj2zRHUFV514Ee/6EKCQiwWYDCTY0N7Az2JDMubYiyoCxNyXXIFj1NDadYvruPUUIbFDRF6ElniIZfeRJahdI4VzXG6+pP0J3O8dmyQhliI9YvrWNoYZVN7AzWRADVh+3Mzppxkrq54tWXLFq2mqRU8T+keSBNLwfaXuzl4YJDjbyYIJZVgGq6oi+OklKWhMIx4SFbRnMIE3z5PlUzeI1fwGEzmCLjCQDJHNu+d9bpCEPIhwQtCPgiFoFAI+Y+P3s+HhVwEchEhH4ZctPiz+G913zqyF4FrljeysjnOVUsbqI0EiAZdAq7N32fMTBKRHaq65XzP2ZCrxPIFD0chcyRDujPNyOEUqaNpdr/Uy8ixNAFHKBSUKLAaaGuIEAsFqEmMBmMeOJPzEhAkLDghBwn5PxFAOLteLxADNK+05P0Pi/a8h+aUfM4jm/GQgtKfzBELuSTSeShAPumhCuGASzKbJxx0yeQKhAIOeU9xRAgFHDK5ArGQi+sI9Y0hsmFIhpRTmqPXKTAQLHBqRzdHYz38LNZJNiaEmoJctrqOppoIb1vZBEB7Y9SOMxgzSyzwZ1FvIkM46BLDoX/nENueOE7HSwNclg6hOcUVoTuRITc6yhZIRkGagrQsi9G+qoalK2txa10CtQHcWnfs5kQcP+RnsE6unuKlPf+W8n8WUoWx+17aw0t6FBIFCokC+eG8f3+4eCs+ThaiWYgiNBMCIFv8ZpHKFRhM5QDF87KojDAYE34c6yBTI7hNAfJ1DguWxli+uo6ahRHaV9VQiMDCuuiM9dWYamSBP0uOdA7ztS/upbXDY8EpSI34I/M64CQpUg3CcKswcoXDSJNLskW49/YVaFC4dnkjdZFgydssjuDGXNzY1A/cqipe0iM/lKcwVCA/kB+7tRV/5vpz5AfyDJxKkx7MM5zOMdJXQPuU/OGsvx3SdNAH+JdoF4LgNgXI1To0LYkSXximdnGExvYoTkuAlZfX40anf8DZmEpmgT9DOnpGeOH1Hm5JxEg+P8RrPz3NqrQf8ilgeJGQWBWk9ao6XvCGWbA4RjTosiAe4revbcfzlAV1kfJ2YgaICG7cxY270Hbp13t5z/9A6M+T7c5xsnOEQn+eQwcGyPXmSHfn0P48wRx4p/IET8HwwSzDwMlx29nuCrmoEFsUJlvvEGsLEVwQItgaZNUVDbjNAZYvrbFykalqdtB2Boz0Zfjqp16l8cU0kYwASgFIrA1Sd3M9fcsdPvgbK6mLBHEc4fRQmqZ4yA5YTpCqkhzM0XloGO3Pc+D1fgp9BYaOpaCvgNNXwOkv4BQuvh23xqXQ5FK7JEK8PUzDshiL1tYQXhSiZVl8RstjxpTLxQ7aWuBPQyFd4F++8AZDP+wjn/TTZrhVOH6lS8Pb63n/bStY1hwrcysrX77g0TuSodBX4OD+AYKDHj0dSejNkzqVIXMqi9vv4WW8C24jHHEpNDrE2qO4CwM0Lo/RuCJGTXuUJWtrcAL24WzmBztLZxYMvjDIm186St/efgD6Vzi866E1ZFeHWNYcpykeKnMLq0fAdfwDunWweEXNeV+Tyxfo6BwmezLHoX0D5E9m6etIot15An0FGC7AiQJDJ3IA9BePHwC4rlBocIgujqALA9SviBJtj9C6Js7adQ049k3NzBM2wp+k/HCe4397nMHnBzk5mOaNaIbU3XX8nx+5gkYL+XkplS1w9ESCzMksRw8M4Z3O0XM4idOTxzuVwx3wkAv8b6IB0NYAbZfX0XZ5DYsvqyXSHiHcHvaPYxhTYlbSmSGpN1N0/lUn6ZMZDg+neOUa5dhVLn9y5xWsa6srd/PMLMgXPAYTOQaPpjh6YIjCiSz9bybhdI7UkQwMnn3gIBhwiAZd4mGXxoVR6lZEiSwNE14SJtweJrw0TGhhyA4em1ljJZ0ZMPiLQY7+9VE0q/Q0w1O3O6TrhbaGCFcsqi1388wsCbgOzfVhmuvDrLqy4aznVJXUcI69r/Sx95Veet8cQU/kiA3kifbnODGQxn1jgIZYkEjQpT4aJOj6F8iFl4aJLIuc+bms+EFgB47NLLLAn4D+Z/rp+nwXQ6kcu5bmeeUm8ALC3Vct5vYNC220VqVEhFhdiC23LWLLbYvwPOVw7wjdwxleerOXk50jZI5liPV5xPoLxPoz1PYpTQWXaHeS8KsuNZEAAUdwRPwPguK3gNEPgcjSCKE2+yAwM8MC/xL6f9ZP1xe7QOHAFocdlwEiREMud1+12GaENGMcR1jdWsPq1hpuWNUMwKHuBPtODNHRM8Jrx4bIFTwCGSXWq8T7ssR7M8T7lJaEQ/Q0xE4nCe4SIkGXeDjgfxAExS8JLSt+ECwPE1kRsdKQmTQL/ItI7ErQ9SU/7Jvub2Vb/hgUBBG477plFvbmkkY/AMAvAQ1n8uzo6GconeONUwmOD6Q4lM6jqv4HQZ8S7/OI9+aI9SqNg0J9WnBOJojvdgkGHGLBAKGAgxNxCC8PE10ZJbLizAdBoMb+tzbnZ38ZF5A5meHIZ4+AB62/08qpWyLkfuaxvDnOf7l7fbmbZ+YhEaEuEuSdVyw46/FMvsCpwQxD6RyHuhMMpfMcPDXM4cE0BU9xM0q8T4n1FYj35qjpTVPfB7UZIXrCJfCSQzzkEgm6BFwh1BIisiLi31ZGiCyPEFoSsmsJjAX++RTSBTr/spPCcIHa62oJ/04jP3nuTQCuWlpf5taZShMOuGMX6F255Mzfl6pyaijD6eE0g6kcb3aP0J/McuB0glS2QDCpxHuVmu48NT054r0eDb1C9LRD5A2HcMAlHHSIhwIEQg7h9rD/AbDC/xCIrIwQbCr9nE2mfEq9pu3HgQfwZ/fdDXxEVdOlbMNEnPzaSTKdGUJLQrT/x3Y+88wBDveMEA8HuHlNS7mbZ6qEiLCoPsKien+OpbevbQXOlIY6e5L0JDKcHEpz8HSCo8MZRlI5ooNKTY/6HwI9HjU9SkNSCJ90iexyiIUChAMOoYBDqDFIZFWE6KookdURoiujhBbbsYFKVbLAF5ElwH8A1qtqSkT+CbgP+Fqp2jARidcS9P1zH+rAd69Oc+JHu0lm8sTDAT59zwYaYnZxlSmv0dLQxvazv22qKt2JDL2JLJ29SboTGTp7RtjTO4KTKX4b6CkQ78lT06PU9ii1px3Cb/rXDgRdh1g4QCQe8L8JrIoQXR0luipKeFkYJ2glofmu1CWdABAVkRz+ehzHS7z/Czo5mGbn4X5WPjwMgNxVx6Hwacj4z9+ypsXC3sxpIsKC2ggLaiNnXQiYzftLVJ4YTNPVn+RQd4KuRJae4TThQaX2dLEs1O1R261ET0Kk0yH6S5eQ6xANuQSCDvWrYsTWRP2DxMVvA3Y18fxSssBX1WMi8jngCP6MwU+q6pPjXyMiDwIPAixbtqxUTQPg29uP0vOjHmKvh1i4qoZTb4/AS2eev+3y1pK2x5iZEgo4LG2KsbQpxvXFlcUAEpk8x/pTHB9M0dWXpG8kx8unhykM5Knp8ag97RU/CJRYj+KcGCbwK4dI0D8+EHSFSFuY8KoIC9fXUn9FnNjaGIF6OzQ4V5WypNMI3AOsBAaAb4vI76nqP46+RlUfBh4Gf2qFUrUN4PipJGu35UkGXBb9/iJeHhkE4LJFtdy+fiELK2CuemPGqwkHuHxRLZePu1JcVRnJFjjcPcLp4TTHBlIcH0hzdCjLyJspak773wJqurPEexV3fxr2D7L/J6dAIBxw8BpcZHmI8KoowVVh2jbWU9MSoiEWojEWtGnBy6iUH8W/DhxW1W4AEfkecBPwjxf9rRLI5j2iv0wSSkH/aqh7Wx3HnvSX17hzwyKuWtpQ3gYaUyIiQk04UDw+cPYxgmQ2TyKdp6M3yUAyy6mBNEOdKQodGZKHUsRP+t8K3G4PunNkt48A0Mcx0rXC8EJheKFDvj2IszzEkvYa3n5ZC2sX1No1LSVSysA/AtwgIjH8ks67gDkxO9qJniTtL/urU712tf+H19WfBGBJo62jagxALBQgFgqcvTLbzWfuZvMeJwdTJI+m6X5tmPShFOmDafKdGWJpj9rDSu5gHsgDKdJ1Qzy+4ASPLQmwcGMda65tZONlTTbr7CwqZQ1/m4h8B3gZ/x1/hWL5ptyO/bSXUBISLcLJJcrLR/oZTueJBF2a7Y/PmAkJBRyWNcehOc4Vm5vHHldPyRzLkDqYYuT1JEOvj5A4lGJgIMNgZ47MwSw818MReni9TnBWhGjZUMvKaxu5/PpmwnV2rcBMqfrpkVWVJ+/fSfe+BPtuD3Jy/ZmzDtYsrOHP7lo3620wptqop2S6MqTeSHH6tSGOvzrE0IERRkZyeOMWJhMHIu0Rmq6sYfk1jSy6qo7IyohdNXwRNj3yRaTfTJPtzJCLQOymWhhIEgm5bFpSz7s3TmAVbmPMpIkjRJZFiCyL0PiuRi4HtKAkOlIceqmPo68M0L8/AV05UkfSHDuS5thPenAcIRYLULMmRsP6OMuubaB+fQ2hRXax2ERUZeAnMnk+9y+vc9PqZjb8vEA6V+DUBpffvXk5kaBLa22YoJ1JYExJiSvUro6xeXWMzfe1A9A7kOa1bT107OhncO8IkWN5vP4ciZ2DnNw5yP5HjxNwHeINQcJrIzRfWcvizfW0XFlLsMFKQeeqysDf0dnP0b4k/9Q9wr9/OkYm59G9KcKyppidMmbMHNLcEOG2O9q57Y52VJX+ZI5XD/RxcvcQfa8NkzuUofZ4gXxvBnoznH5hkH10EXCFQGsId3WY5g21NF9Zw8prGonVVPeHQFUGfqgY6o1HPfq704w0C20b6izsjZnDRISmeIh3XL0Irl4EgOcpxwdTvLF/gL7dCXr3DFM4nCF2okD+ZAZOZhj5xRBHgJcdyC10cVdFiK+LU7c+RvsVdSxrjlMTro4orI5eniOT99chbT3ocXo4Q/e1LptsmUJj5h3HEdobY7TfGIMb/cdUlZP9aU6+PkT37gT9e4bJv5mBriyhEwU4MUL6FyOkgWMR+OkiB10VIrg2SsuVNbQtjLOwLsLSpiixUGVFZGX1ZoLSuQJ4SuuhAtk8nF4b4PKFFvjGVAIRoa0pStuNUbhx4djj2ZE83XuG6Xp5gKG9I2QPpMj05Qh1FtCONDydJiv97G0WftXmMNTm4KwOs2htDU01YZY0RFnWFGNJY3TeHuOrysBPZgvUnVKCKUg1CCwOsrIlXu5mGWNmUSgeYMn1jSy5vhHwvwnkunMM70lwatcwg3sSJA4myaYKtL7ukX4thz6ZIxdJcHqRwxttDkNtQmKxS0tLhLb6KG31/s/VC+K01oTn/JlCVRv4TZ3+yb69yx3+3TvXELLzeo2pKiJCaEGI5gVNNL/Tn1TOy3ikDqZIvp4ksW+E3leHSfZkyfV7pE4XSG0vkMnnSDRnGGwbZkebw2CbkGoQXNdhZUucVa1x2htjLGuK0d4YnVMfAlUZ+OlcgaYjHnXRIHfft+KsqWSNMdXLCTvEN8SJb4jTSisrit8CkvuTY7eRgynSmTyZ4x6ZTo9UrsCQk6e3FQYX53ixbYinFgleUIiHA2xYXMd91y+jPlr+M4SqMvBTgzkaTng0tUa54jab9tgYc36j3wJCC0I03NoAgJctfgsY9yGQ789TyHmMHCiQ2lMgVfDobihwqjXH4cUZvtQ1QjIKG5c0cO81S4gEy7OOQFUGvhzKIgqhNRHcmC3gYIyZOCfkEF8fJ77eP+6n478F7EsysneE9OE0y1XJnPA49GqC/D8PkqoXji9O8PkVp7j6nYu46e0LCQVcXEdKdhC4KgPfPZwFILbBDtQaY6bnfN8CCqkCydeTJPcmCewYoOOlftrEZeBQjtS+ND2Pd/C9cAeDbQ6DSxxar6rj6rcv4Oq1TYQDszcIrcrADx7JAVCzLlbmlhhjKpEbdandXEvt5loW3r+QzQUl3ZlmZM8Ih7b10bmtD6c3R+sRj+YOD37Rxxtf7ePVhS7ZFQFGlrr86Z9ePePrCFdd4Ht5j8gxf+77hg01ZW6NMaYaiCtEV/kLwrfc3cLbgGxPluTeJL2vDtO5rY/+AyPIiQKxEwXqXgT945mfybjqAj99OA0ZJdkg1LSEy90cY0yVCrWECN3ql4FW/+FSCqkCJ14ZJLkvSSjrHyuYaVUX+IP7/GXXRha7NneOMWbOcKMu7Tc1wU1Nl37xFFVd4iXe8JcuzC2pus86Y0yVK1ngi8jlIrJz3G1IRP6oFPv+/ivH+Msf7yWb90geTgGQX2yBb4ypLqVc0/Z1YDOAiLjAMeCxUuz7R7uOA/ByRx/SmQFAlpT/qjdjjCmlcpV03gUcUtXOUu40fTxDPl0gXSuEbDUcY0yVKVfg3wdsPfdBEXlQRLaLyPbu7u4Z32mmI0PBUxItQjRkV9gaY6pLyQNfRELA+4Bvn/ucqj6sqltUdUtr68zPceN1Zf3AbxViZZrLwhhjyqUcI/y7gJdV9VSpd+ydyJEvKMlGh/qYlXSMMdWlHIH/u5ynnDNbVM9creadzpEreCQbhYZYqFRNMMaYOaGkgS8iceA3gO+Vap95rxj4qnDKD/xUg9AwB+amNsaYUirpyeiqOgI0l3KfuYK/slVoBLy0RzoM+YjQaCN8Y0yVqfirj3IFf4Qf61fynjJUXNyqwWr4xpgqU/FTK+SLI/zogEeu4DFQ689fXRexwDfGVJeKD/yxEf6Aki8oqQahPhrEcebOwsLGGFMKVRD4oyN8P/iTDWLlHGNMVaqawI8M+4GfqbUzdIwx1akKAt8P+nAx8NO1QkPcztAxxlSfig/8vOfh5JVQCjwXsnFotsA3xlShig/8XEEJJ/zRfTYuuK7DzatbytwqY4wpvSo4D98jPAzhoMPCtbXcf996YqGK77YxxrxFFYzwPSLDSjTocvm6Bgt7Y0zVqoLAV8LDiiNCqNVq98aY6lXxgZ8veIQTiggEW+10TGNM9ar4wM8VlEhxhB9sscA3xlSvKgh8j/BwcYRvgW+MqWJVEfihpCIiBBrtgK0xpnpVfODncx7BNDgCgToLfGNM9Sr1ilcNIvIdEdkvIvtE5MbZ3mc+UUAUJO4irs2QaYypXqUe8n4ReEJVf0dEQkBstndYGMgjgFPvzvaujDFmTitZ4ItIPXAr8GEAVc0C2dnerw4X/MCvs8A3xlS3UpZ0VgLdwP8SkVdE5H8WFzWfVd5gAQC3wer3xpjqVsrADwDXAH+nqlcDI8Anxr9ARB4Uke0isr27u3tGdqrDxcC3Eb4xpsqVMvC7gC5V3Vb893fwPwDGqOrDqrpFVbe0trbOzF6H/MAPNtg5+MaY6laywFfVk8BREbm8+NC7gL2zvuOEv+JVoN5KOsaY6lbqFPz3wDeLZ+i8CXxktncow37gB62Gb4ypcpdMQRH5G+BRVf3FdHemqjuBLdPdzmQ4xcAPN1pJxxhT3SZS0jkAfE5EOkTkv4vI1bPdqJnkjBRH+DatgjGmyl0y8FX1i6p6I3Ab0Av8ffFK2U+KyGWz3sJpchM2wjfGGJjEQVtV7VTVzxZPqfxd4DeBfbPVsJng5T0kq6hAuNZG+MaY6jbhwBeRgIjcLSLfBB4HXgfunbWWzQBvxMNTyIchFKz4eeKMMeaiJnLQ9jfwR/TvBl4EvgU8qKojs9y2acsn8qgq+bAQcCzwjTHVbSJ1jj8DHgX+WFX7Z7k9M6qvO40qODGHUMAC3xhT3S4Z+Kr6awDi+z1glap+WkSWAYtU9cXZbuRkpbIF/u65QzQf8XCBiF1la4wxk7rS9m+BG/HLOwDDwN/MeItmwCtH+9lzbJB9hwYAiFvgG2PMpK60fZuqXiMirwCoan/xitk5Jx7yuxXIKAC1jeFyNscYY+aEyYzwcyLiAgogIq2ANyutmiYpLmwVyPg/61ss8I0xZjKB/yXgMWCBiPwV8Dzw32alVdPk+QN7Aln/TlNzpIytMcaYuWHCJR1V/aaI7MCf5VKA31TVOXnhlaof9MG0/++6pjlZeTLGmJKa1OWnqrof2D9LbZkxXjHwAxlY0hjFrbHFT4wxZiIXXv3wYs+r6vtmrjkzo5j3LI1GaKkJWuAbYwwTG+HfCBwFtgLb8Ms5c9poDd9N+3fcuAW+McZMJPAXAaPTK9wP/DOwVVX3zGbDpmO0pOOk/ZOILPCNMWZi0yMXVPUJVf0QcANwEHhWRP5w1ls3RWcCvzjCt5KOMcZM7KCtiISB9+CP8ldw5hTNSRGRDvwrdAtAXlVnZfWr0Rq+m1IIghO3eXSMMWYiB22/DlwJ/AT4lKq+Ns19vlNVe6a5jYtSBckrkgdccMIW+MYYM5ER/u8BI8BDwEMiUhw/I4Cqat1sNW6qPFUCWb+BbtxFZM4fZzbGmFk3kaHvLUCdqtYWb3XFW+0Uwl6BJ0Vkh4g8eO6TIvKgiGwXke3d3d2T3PQZfuArCLgxq98bYwxMLPA/COwQkW+JyIdFZNE09neLql4D3AV8TERuHf+kqj6sqltUdUtra+uUd6IKbs4f4TsRK+cYYwxMbD78fwcgIlfgB/XXRKQeeAZ4AviFqhYmsjNVPVb8eVpEHgOuB/51im2/IE8VJ+ffl7CVc4wxBia3iPl+Vf28qt4J/Br+5Gnvx78Y65JEJC4itaP3gduB6R4AvkBbwc0rgtgI3xhjiiY1l84oVU3hn7Xzk0n82kLgseIB1ADwqKo+MZX9X4qnipsDxEo6xhgzakqBPxWq+iZwVSn25Sm4xbN07JRMY4zxTTrwReTbQBwI4V9ApcUyz5yhqrj54tW2NsI3xhhgcgugAKCq7we2A3cAdwI/nelGTZc3epaOgBO1wDfGGJh6SecyYAmQA1bNXHNmhlKs4WMlHWOMGTXVwP8k8B/wL6T68sw1Z2Z4Ck7OSjrGGDPeVM/SeR34kxluy4zxPC2WdOy0TGOMGTWlwBeR/wRswD8GoKp6/4y2aprOKulY4BtjDDD1ko6o6r+Z0ZbMIM8DN6d2WqYxxowz1cBfLSLvx59FE1WdzAVYs06xuXSMMeZcUw3854BY8aaXeG3JjdbwwQLfGGNGTeXCq5uBbs4sZj73Al8VJ+9Pj2wlHWOM8U1lhN8ENHLmoi1v5pozM6ykY4wxbzWVK21/BCzCX+P23fiTos0pY5OnIXalrTHGFE01DVtV9f2q+gHmZOAXz9Kxko4xxoyZSg3/PUCbiHyw+NB0VsCaFaqKk/fvW0nHGGN8l0xDEdlwzkMt+BOmOcXb3Js8Le/h5v3J0yRkK14ZYwxMbIT/DeAaABF5QFX/5+gTIhJT1eRsNW7KssUTh8IOxQVXjDGm6k2k3jE+MT96znM/n8G2zBjN+IFvo3tjjDljIoE//jz7cxN00gVyEXFF5BUR+fFkf3fCRgPfDtgaY8yYiSTiIhH5sIhczVsDfyoXXT0E7JvC702YZouXBoRthG+MMaMmUsP/C+Ba4CNAu4jsxQ/s/fgHcCdMRNrxz9//K+A/Tqqlk1Ec4dspmcYYc8YlA19VHx7/72JobwQ2Af86yf19Afi/gdrzPSkiDwIPAixbtmySmx4nayUdY4w516TPw1fVLqALeHwyvyci7wVOq+oOEXnHBbb9MPAwwJYtW6Y8R4/m7KCtMcacq5RD4JuB94lIB/At4NdE5B9nZU/5Yg0/aIFvjDGjShb4qvpnqtquqiuA+4CnVfX3ZmVno1MjByzwjTFmVEUWuTVfLOnYCN8YY8ZMdQGUaVHVZ4FnZ2v7UqzhO8GK/DwzxpgpqchEHDtoayN8Y4wZU5GBL3k7S8cYY85VkYFvNXxjjHmrigx8RufCtxq+McaMqcxEzI8etLURvjHGjKrIwBc7aGuMMW9RkYHP2EHbyuyeMcZMRWUmotXwjTHmLSozEa2Gb4wxb1GRgT96Hr5r5+EbY8yYigx8CsWZlW3yNGOMGVORgS/FGn7ADtoaY8yYikxEm1rBGGPeqqID37URvjHGjKnMRCyWdOzCK2OMOaMiA99G+MYY81YVmYhS8H+6duGVMcaMKVkiikhERF4UkV0iskdEPjVr+xqbHtkC3xhjRpVyicMM8GuqmhCRIPC8iDyuqi/M+J6KNXwr6RhjzBklC3xVVSBR/GeweNPZ2JdTUDzAidhBW2OMGVXSIbCIuCKyEzgN/FRVt53z/IMisl1Etnd3d09pH6o6duGVjfCNMeaMkiaiqhZUdTPQDlwvIlee8/zDqrpFVbe0trZObR8FRQF1wHVshG+MMaPKMgRW1QHgGeDOGd92TgHFc0GwwDfGmFGlPEunVUQaivejwG8A+2d6P5pVVPED3/LeGGPGlPIsnTbgH0TExf+g+SdV/fFM78TLef5PV3As8Y0xZkwpz9J5Fbh61veT82v4ngtWwjfGmDMq7jQWzSsoeAFshG+MMeNUXOB7Wb+ko1bDN8aYs1Rg4PvXcvkHbS3xjTFmVMUFfmF0hG/LGxpjzFkqL/AzZ0o6xhhjzqi4wB89LdNG+MYYc7YKDHy/hq+lvMLAGGPmgcoLfKvhG2PMeVVc4BeKI3xcC3xjjBmv4gL/TA2/zA0xxpg5puJiMXpNLbt+M0iwOVjuphhjzJxScYEfaAnQt8KlPmrnZRpjzHiVV9IplvDtKltjjDlbBQa+n/g2U6Yxxpyt4ko6o4FvA3xj5p5cLkdXVxfpdLrcTZn3IpEI7e3tBIMTP15ZcYHP6FmZNsQ3Zs7p6uqitraWFStWWNl1GlSV3t5eurq6WLly5YR/rwJLOqP37I/JmLkmnU7T3NxsYT9NIkJzc/OkvymVck3bpSLyjIjsFZE9IvLQbOzHavjGzG0W9jNjKv8dS1nSyQN/rKovi0gtsENEfqqqe2dyJ2cC3/6ojDFmvJKN8FX1hKq+XLw/DOwDlsz8fvyfNsI3xlzI97//fUSE/fv3z8j2vvnNb7Jp0yY2btzITTfdxK5du8aee+KJJ7j88stZs2YNn/nMZ8Ye/8pXvsKaNWsQEXp6es7a3rPPPsvmzZvZsGEDt91224y0EcpUwxeRFfgLmm875/EHRWS7iGzv7u6e0rbVzsM3xlzC1q1bueWWW9i6deuMbG/lypU899xz7N69mz//8z/nwQcfBKBQKPCxj32Mxx9/nL1797J161b27vWLGjfffDNPPfUUy5cvP2tbAwMDfPSjH+WHP/whe/bs4dvf/vaMtBHKcJaOiNQA3wX+SFWHxj+nqg8DDwNs2bJFz/Prl1Sw0zKNmRd+/2svzcp2H/nwdRd9PpFI8Pzzz/PMM89w991386lPfYpnn32WT37ykzQ0NLB7924+8IEPsHHjRr74xS+SSqX4/ve/z+rVq+nu7uYP/uAPOHLkCABf+MIXuPnmm7npppvGtn/DDTfQ1dUFwIsvvsiaNWtYtWoVAPfddx8/+MEPWL9+PVdfffV52/foo49y7733smzZMgAWLFgw7f8mo0o6wheRIH7Yf1NVvzcb+7AavjHmYn7wgx9w5513ctlll9Hc3MyOHTsA2LVrF1/96lfZt28f3/jGNzhw4AAvvvgiDzzwAF/+8pcBeOihh/j4xz/OSy+9xHe/+10eeOCBt2z/kUce4a677gLg2LFjLF26dOy59vZ2jh07dtH2HThwgP7+ft7xjndw7bXX8vWvf32mul66Eb74NZZHgH2q+teztR+r4RszP1xqJD5btm7dykMP+ScJ3nfffWzdupX3vve9XHfddbS1tQGwevVqbr/9dgA2btzIM888A8BTTz01VpIBGBoaIpFIUFNTA8AzzzzDI488wvPPPz/l9uXzeXbs2MHPfvYzUqkUN954IzfccAOXXXbZlLc5qpQlnZuBDwK7RWRn8bH/pKo/mcmdqI3wjTEX0NfXx9NPP83u3bsREQqFAiLCe97zHsLh8NjrHMcZ+7fjOOTzeQA8z+OFF14gEom8ZduvvvoqDzzwAI8//jjNzc0ALFmyhKNHj469pquriyVLLn6uSnt7O83NzcTjceLxOLfeeiu7du2akcAv5Vk6z6uqqOomVd1cvM1o2INNnmaMubDvfOc7fPCDH6Szs5OOjg6OHj3KypUr+fnPfz6h37/99tvHyjsAO3fuBODIkSPce++9fOMb3zgrmK+77jreeOMNDh8+TDab5Vvf+hbve9/7LrqPe+65h+eff558Pk8ymWTbtm2sW7du8p09jwq80tYO2hpjzm/r1q381m/91lmP/fZv//aEz9b50pe+xPbt29m0aRPr16/nq1/9KgCf/vSn6e3t5aMf/SibN29my5YtAAQCAb7yla9wxx13sG7dOj7wgQ+wYcOGsW21t7fT1dXFpk2bxo4HrFu3jjvvvJNNmzZx/fXX88ADD3DllVfOSP9ltAQy12zZskW3b98+6d/bc3yQv37yAFe01fKnd1wxCy0zxkzVvn37Zmy0as7/31NEdqjqlvO9vuJG+GcO2toQ3xhjxqvYwLcavjHGnK3iAt8mTzPGmPOr2MAXmx7ZGGPOUoGB7/+0Eb4xxpyt4gJ/dMkrxxLfGGPOUnGBf+bCq/K2wxgzN7muy+bNm8duHR0dY5OfdXR08Oijj5a5hbOn4ta09TybWsEYc2HRaHTsCtlRv/zlL4EzgX///feXoWWzr/ICf3SEX95mGGMuYffdu2dluxt/tHHSv1NTU0MikeATn/gE+/btY/PmzXzoQx/i4x//+Cy0sHwqLvBHrxx2rYZvjDmPVCrF5s2bAX/hkscee2zsuc985jN87nOf48c//nGZWje7Ki/wiz/twitj5rapjMRnwvlKOtWiAg/ajp6Hb4wxZryKC/yCZ1faGmOmpra2luHh4XI3Y9ZUXOCPlnTsPHxjzGRt2rQJ13W56qqr+PznP1/u5sy4iqvhBx2H2kiASMAtd1OMMXNQIpG44GPBYJCnn3661E0qmVKuafv3wHuB06o6M7P5n8cta1u4ZW3LbG3eGGPmrVKWdL4G3FnC/RljjBmnlGva/ivQV6r9GWPmprm6yt58M5X/jnPqoK2IPCgi20Vke3d3d7mbY4yZYZFIhN7eXgv9aVJVent7iUQik/q9OXXQVlUfBh4Gf03bMjfHGDPDRhfttgHd9EUiEdrb2yf1O3Mq8I0xlS0YDLJy5cpyN6NqzamSjjHGmNlTssAXka3Ar4DLRaRLRH6/VPs2xhhTwpKOqv5uqfZljDHmrWSuHi0XkW6gc4IvbwF6ZrE5c4H1sXJUQz+tj+WzXFVbz/fEnA38yRCR7aq6pdztmE3Wx8pRDf20Ps5NdtDWGGOqhAW+McZUiUoJ/IfL3YASsD5Wjmrop/VxDqqIGr4xxphLq5QRvjHGmEuwwDfGmCox7wNfRO4UkddF5KCIfKLc7ZkpItIhIrtFZKeIbC8+1iQiPxWRN4o/G8vdzskQkb8XkdMi8tq4x87bJ/F9qfi+vioi15Sv5RN3gT7+hYgcK76XO0Xk3eOe+7NiH18XkTvK0+rJEZGlIvKMiOwVkT0i8lDx8Yp5Ly/Sx/n9XqrqvL0BLnAIWAWEgF3A+nK3a4b61gG0nPPYfwc+Ubz/CeCz5W7nJPt0K3AN8Nql+gS8G3gcEOAGYFu52z+NPv4F8Cfnee364t9sGFhZ/Ft2y92HCfSxDbimeL8WOFDsS8W8lxfp47x+L+f7CP964KCqvqmqWeBbwD1lbtNsugf4h+L9fwB+s3xNmTw9/yI4F+rTPcDX1fcC0CAibSVp6DRcoI8Xcg/wLVXNqOph4CD+3/ScpqonVPXl4v1hYB+whAp6Ly/SxwuZF+/lfA/8JcDRcf/u4uJvynyiwJMiskNEHiw+tlBVTxTvnwQWlqdpM+pCfaq09/YPi+WMvx9Xipv3fRSRFcDVwDYq9L08p48wj9/L+R74lewWVb0GuAv4mIjcOv5J9b9HVtQ5tZXYp6K/A1YDm4ETwP9b1tbMEBGpAb4L/JGqDo1/rlLey/P0cV6/l/M98I8BS8f9u7342LynqseKP08Dj+F/PTw1+lW4+PN0+Vo4Yy7Up4p5b1X1lKoWVNUD/j/OfNWft30UkSB+EH5TVb9XfLii3svz9XG+v5fzPfBfAtaKyEoRCQH3AT8sc5umTUTiIlI7eh+4HXgNv28fKr7sQ8APytPCGXWhPv0Q+D+KZ3jcAAyOKxfMK+fUq38L/70Ev4/3iUhYRFYCa4EXS92+yRIRAR4B9qnqX497qmLeywv1cd6/l+U+ajzdG/4ZAAfwj4r/53K3Z4b6tAr/iP8uYM9ov4Bm4GfAG8BTQFO52zrJfm3F/xqcw69x/v6F+oR/RsffFN/X3cCWcrd/Gn38RrEPr+IHQ9u41//nYh9fB+4qd/sn2Mdb8Ms1rwI7i7d3V9J7eZE+zuv30qZWMMaYKjHfSzrGGGMmyALfGGOqhAW+McZUCQt8Y4ypEhb4xhhTJSzwjTGmSljgG2NMlbDAN1VBRL4sIp3T3Mb/JSIqIuvGPbaveGXlVLb3v8bNq75TRE6KyERn2jRm0izwTcUrznb4TiA0OmXFFG3Ev+LyPcXtRvBnhOy4wH7fISJfu9DGVPUjqrpZVTfjX6afBz48jfYZc1EW+KYafAr4S2AvsGEa29kEfJZi4OMverFfp3m5uoi0AE8A/1VV5/1cUGbussA3FU1ENgBXAv8bfxGLK8/zmp+fU1oZvf36OS9djz8h2AIRqccf8b86zfbFgB8B/6Sq/2M62zLmUgLlboAxs+wvgf+iqioi+zjPCF9V336pjYjIUqBXVVMi8lPgDvwR/+7zvHYb/lJ3NUCTiOwsPvX/qOq/jHudi79K235V/fNJ98yYSbLANxVLRN4G3AlcLSJ/A0Q4f0D/HH/d0nP9iao+Vby/cdzv/gT4N/jrnn7/3F9S1bcVt/sO4MOq+uELNPFvgSDwbyfSH2OmywLfVLL/Btw9GtoishB45dwXTWSEz9mj+eeA/wFEOc8HyESIyCeBa4F3qGp+KtswZrKshm8qUrH+Hho3QkdVTwE1ItI0hU2OjfBVNYNfu8+q6sAU2rYC+Av8+eOfH3fM4H9PoV3GTJjNh2+MMVXCRvjGGFMlLPCNMaZKWOAbY0yVsMA3xpgqYYFvjDFVwgLfGGOqhAW+McZUif8fLKN5S8wF8OUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "Masses['Eapprox']  = ytilde\n",
     "# Generate a plot comparing the experimental with the fitted values values.\n",
@@ -4093,9 +3823,7 @@
   {
    "cell_type": "markdown",
    "id": "8a268c03",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Adding error analysis and training set up\n",
     "\n",
@@ -4105,12 +3833,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 5,
    "id": "ea27e8ce",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def R2(y_data, y_model):\n",
@@ -4120,22 +3845,25 @@
   {
    "cell_type": "markdown",
    "id": "08794dc7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and we would be using it as"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 6,
    "id": "f4ea9b85",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9549351910143222\n"
+     ]
+    }
+   ],
    "source": [
     "print(R2(Energies,ytilde))"
    ]
@@ -4143,22 +3871,25 @@
   {
    "cell_type": "markdown",
    "id": "1a98cd90",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can easily add our **MSE** score as"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 7,
    "id": "67443c25",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.022022882954618378\n"
+     ]
+    }
+   ],
    "source": [
     "def MSE(y_data,y_model):\n",
     "    n = np.size(y_model)\n",
@@ -4170,22 +3901,37 @@
   {
    "cell_type": "markdown",
    "id": "5acb03ba",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and finally the relative error as"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 8,
    "id": "6db08232",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A        \n",
+      "4    0       1.093247\n",
+      "5    2       0.275341\n",
+      "6    7       0.060080\n",
+      "7    12      0.019587\n",
+      "8    17      0.117986\n",
+      "               ...   \n",
+      "264  3297    0.000148\n",
+      "265  3303    0.001263\n",
+      "266  3310    0.002948\n",
+      "269  3331    0.000599\n",
+      "270  3337    0.002526\n",
+      "Name: Ebinding, Length: 264, dtype: float64\n"
+     ]
+    }
+   ],
    "source": [
     "def RelativeError(y_data,y_model):\n",
     "    return abs((y_data-y_model)/y_data)\n",
@@ -4195,9 +3941,7 @@
   {
    "cell_type": "markdown",
    "id": "e59fbc6c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The $\\chi^2$ function\n",
     "\n",
@@ -4217,9 +3961,7 @@
   {
    "cell_type": "markdown",
    "id": "509f82a9",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\chi^2(\\boldsymbol{\\beta})=\\frac{1}{n}\\sum_{i=0}^{n-1}\\frac{\\left(y_i-\\tilde{y}_i\\right)^2}{\\sigma_i^2}=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)^T\\frac{1}{\\boldsymbol{\\Sigma^2}}\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)\\right\\},\n",
@@ -4229,9 +3971,7 @@
   {
    "cell_type": "markdown",
    "id": "23973e86",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where the matrix $\\boldsymbol{\\Sigma}$ is a diagonal matrix with $\\sigma_i$ as matrix elements."
    ]
@@ -4239,9 +3979,7 @@
   {
    "cell_type": "markdown",
    "id": "9ff81a4f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The $\\chi^2$ function\n",
     "\n",
@@ -4251,9 +3989,7 @@
   {
    "cell_type": "markdown",
    "id": "639f9b51",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_j} = \\frac{\\partial }{\\partial \\beta_j}\\left[ \\frac{1}{n}\\sum_{i=0}^{n-1}\\left(\\frac{y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}}{\\sigma_i}\\right)^2\\right]=0,\n",
@@ -4263,9 +3999,7 @@
   {
    "cell_type": "markdown",
    "id": "1e570605",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "which results in"
    ]
@@ -4273,9 +4007,7 @@
   {
    "cell_type": "markdown",
    "id": "375073cd",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_j} = -\\frac{2}{n}\\left[ \\sum_{i=0}^{n-1}\\frac{x_{ij}}{\\sigma_i}\\left(\\frac{y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}}{\\sigma_i}\\right)\\right]=0,\n",
@@ -4285,9 +4017,7 @@
   {
    "cell_type": "markdown",
    "id": "0cfa8a7c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "or in a matrix-vector form as"
    ]
@@ -4295,9 +4025,7 @@
   {
    "cell_type": "markdown",
    "id": "318e32a7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{A}^T\\left( \\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{\\beta}\\right).\n",
@@ -4307,9 +4035,7 @@
   {
    "cell_type": "markdown",
    "id": "d2b3cba7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where we have defined the matrix $\\boldsymbol{A} =\\boldsymbol{X}/\\boldsymbol{\\Sigma}$ with matrix elements $a_{ij} = x_{ij}/\\sigma_i$ and the vector $\\boldsymbol{b}$ with elements $b_i = y_i/\\sigma_i$."
    ]
@@ -4317,9 +4043,7 @@
   {
    "cell_type": "markdown",
    "id": "de645a59",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The $\\chi^2$ function\n",
     "\n",
@@ -4329,9 +4053,7 @@
   {
    "cell_type": "markdown",
    "id": "bd4e345b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{A}^T\\left( \\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{\\beta}\\right),\n",
@@ -4341,9 +4063,7 @@
   {
    "cell_type": "markdown",
    "id": "f2bd166f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "as"
    ]
@@ -4351,9 +4071,7 @@
   {
    "cell_type": "markdown",
    "id": "3921d291",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{A}^T\\boldsymbol{b} = \\boldsymbol{A}^T\\boldsymbol{A}\\boldsymbol{\\beta},\n",
@@ -4363,9 +4081,7 @@
   {
    "cell_type": "markdown",
    "id": "98cb07da",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and if the matrix $\\boldsymbol{A}^T\\boldsymbol{A}$ is invertible we have the solution"
    ]
@@ -4373,9 +4089,7 @@
   {
    "cell_type": "markdown",
    "id": "05b99af8",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{\\beta} =\\left(\\boldsymbol{A}^T\\boldsymbol{A}\\right)^{-1}\\boldsymbol{A}^T\\boldsymbol{b}.\n",
@@ -4385,9 +4099,7 @@
   {
    "cell_type": "markdown",
    "id": "636409fa",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The $\\chi^2$ function\n",
     "\n",
@@ -4397,9 +4109,7 @@
   {
    "cell_type": "markdown",
    "id": "68ef36dc",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{H} =  \\left(\\boldsymbol{A}^T\\boldsymbol{A}\\right)^{-1},\n",
@@ -4409,9 +4119,7 @@
   {
    "cell_type": "markdown",
    "id": "d3d9722d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "we have then the following expression for the parameters $\\beta_j$ (the matrix elements of $\\boldsymbol{H}$ are $h_{ij}$)"
    ]
@@ -4419,9 +4127,7 @@
   {
    "cell_type": "markdown",
    "id": "061a1c64",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\beta_j = \\sum_{k=0}^{p-1}h_{jk}\\sum_{i=0}^{n-1}\\frac{y_i}{\\sigma_i}\\frac{x_{ik}}{\\sigma_i} = \\sum_{k=0}^{p-1}h_{jk}\\sum_{i=0}^{n-1}b_ia_{ik}\n",
@@ -4431,9 +4137,7 @@
   {
    "cell_type": "markdown",
    "id": "b28cc402",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We state without proof the expression for the uncertainty  in the parameters $\\beta_j$ as (we leave this as an exercise)"
    ]
@@ -4441,9 +4145,7 @@
   {
    "cell_type": "markdown",
    "id": "4c1ffa0b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\sigma^2(\\beta_j) = \\sum_{i=0}^{n-1}\\sigma_i^2\\left( \\frac{\\partial \\beta_j}{\\partial y_i}\\right)^2,\n",
@@ -4453,9 +4155,7 @@
   {
    "cell_type": "markdown",
    "id": "af65117b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "resulting in"
    ]
@@ -4463,9 +4163,7 @@
   {
    "cell_type": "markdown",
    "id": "1ec0350a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\sigma^2(\\beta_j) = \\left(\\sum_{k=0}^{p-1}h_{jk}\\sum_{i=0}^{n-1}a_{ik}\\right)\\left(\\sum_{l=0}^{p-1}h_{jl}\\sum_{m=0}^{n-1}a_{ml}\\right) = h_{jj}!\n",
@@ -4475,9 +4173,7 @@
   {
    "cell_type": "markdown",
    "id": "bd81a867",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The $\\chi^2$ function\n",
     "The first step here is to approximate the function $y$ with a first-order polynomial, that is we write"
@@ -4486,9 +4182,7 @@
   {
    "cell_type": "markdown",
    "id": "353f76f6",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "y=y(x) \\rightarrow y(x_i) \\approx \\beta_0+\\beta_1 x_i.\n",
@@ -4498,9 +4192,7 @@
   {
    "cell_type": "markdown",
    "id": "07ac6252",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "By computing the derivatives of $\\chi^2$ with respect to $\\beta_0$ and $\\beta_1$ show that these are given by"
    ]
@@ -4508,9 +4200,7 @@
   {
    "cell_type": "markdown",
    "id": "7e14dc03",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_0} = -2\\left[ \\frac{1}{n}\\sum_{i=0}^{n-1}\\left(\\frac{y_i-\\beta_0-\\beta_1x_{i}}{\\sigma_i^2}\\right)\\right]=0,\n",
@@ -4520,9 +4210,7 @@
   {
    "cell_type": "markdown",
    "id": "39166168",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and"
    ]
@@ -4530,9 +4218,7 @@
   {
    "cell_type": "markdown",
    "id": "fd4f4bbf",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_1} = -\\frac{2}{n}\\left[ \\sum_{i=0}^{n-1}x_i\\left(\\frac{y_i-\\beta_0-\\beta_1x_{i}}{\\sigma_i^2}\\right)\\right]=0.\n",
@@ -4542,9 +4228,7 @@
   {
    "cell_type": "markdown",
    "id": "ccb858f6",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The $\\chi^2$ function\n",
     "\n",
@@ -4555,9 +4239,7 @@
   {
    "cell_type": "markdown",
    "id": "1664e71a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\gamma =  \\sum_{i=0}^{n-1}\\frac{1}{\\sigma_i^2},\n",
@@ -4567,9 +4249,7 @@
   {
    "cell_type": "markdown",
    "id": "dc691b69",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\gamma_x =  \\sum_{i=0}^{n-1}\\frac{x_{i}}{\\sigma_i^2},\n",
@@ -4579,9 +4259,7 @@
   {
    "cell_type": "markdown",
    "id": "420f165f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\gamma_y = \\sum_{i=0}^{n-1}\\left(\\frac{y_i}{\\sigma_i^2}\\right),\n",
@@ -4591,9 +4269,7 @@
   {
    "cell_type": "markdown",
    "id": "8d3dd5d9",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\gamma_{xx} =  \\sum_{i=0}^{n-1}\\frac{x_ix_{i}}{\\sigma_i^2},\n",
@@ -4603,9 +4279,7 @@
   {
    "cell_type": "markdown",
    "id": "14d90403",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\gamma_{xy} = \\sum_{i=0}^{n-1}\\frac{y_ix_{i}}{\\sigma_i^2},\n",
@@ -4615,9 +4289,7 @@
   {
    "cell_type": "markdown",
    "id": "cdcd2f57",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "we obtain"
    ]
@@ -4625,9 +4297,7 @@
   {
    "cell_type": "markdown",
    "id": "8013559b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\beta_0 = \\frac{\\gamma_{xx}\\gamma_y-\\gamma_x\\gamma_y}{\\gamma\\gamma_{xx}-\\gamma_x^2},\n",
@@ -4637,9 +4307,7 @@
   {
    "cell_type": "markdown",
    "id": "dbbb8318",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\beta_1 = \\frac{\\gamma_{xy}\\gamma-\\gamma_x\\gamma_y}{\\gamma\\gamma_{xx}-\\gamma_x^2}.\n",
@@ -4649,9 +4317,7 @@
   {
    "cell_type": "markdown",
    "id": "73f53c6d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This approach (different linear and non-linear regression) suffers\n",
     "often from both being underdetermined and overdetermined in the\n",
@@ -4662,9 +4328,7 @@
   {
    "cell_type": "markdown",
    "id": "4514c016",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Fitting an Equation of State for Dense Nuclear Matter\n",
     "\n",
@@ -4689,22 +4353,44 @@
   {
    "cell_type": "markdown",
    "id": "6104f6f4",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The code"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 9,
    "id": "aae219de",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean squared error: 12.36\n",
+      "Variance score: 1.00\n",
+      "Mean absolute error: 2.83\n",
+      "[   0.          618.32047562 -861.13519106 1404.91549644] -11.057088709962727\n",
+      "Mean squared error: 197.93\n",
+      "Variance score: 1.00\n",
+      "Mean absolute error: 11.69\n",
+      "[  0.          28.18220995 282.79902342 842.30879705] 12.946893955210385\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Common imports\n",
     "import os\n",
@@ -4796,9 +4482,7 @@
   {
    "cell_type": "markdown",
    "id": "d0e3bb71",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The above simple polynomial in density $\\rho$ gives an excellent fit\n",
     "to the data. \n",
@@ -4811,9 +4495,7 @@
   {
    "cell_type": "markdown",
    "id": "6e8f8820",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Splitting our Data in Training and Test data\n",
     "\n",
@@ -4831,13 +4513,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 21,
    "id": "5436a04b",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training R2\n",
+      "0.9999871210964857\n",
+      "Training MSE\n",
+      "4.810022933346783\n",
+      "Test R2\n",
+      "0.999972658732442\n",
+      "Test MSE\n",
+      "17.53673673563248\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import numpy as np\n",
@@ -4908,9 +4602,7 @@
   {
    "cell_type": "markdown",
    "id": "140acafd",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Exercises\n",
     "Here are three possible exercises for weeks 34 and  35."
@@ -4919,9 +4611,7 @@
   {
    "cell_type": "markdown",
    "id": "17a2199f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Exercise 1: Setting up various Python environments\n",
     "\n",
@@ -4988,9 +4678,7 @@
   {
    "cell_type": "markdown",
    "id": "a84cc2c0",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Exercise 2: making your own data and exploring scikit-learn\n",
     "\n",
@@ -5002,10 +4690,7 @@
    "cell_type": "code",
    "execution_count": 48,
    "id": "5cf897b5",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = np.random.rand(100,1)\n",
@@ -5015,9 +4700,7 @@
   {
    "cell_type": "markdown",
    "id": "67144260",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "1. Write your own code (following the examples under the [regression notes](https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter1.html)) for computing the parametrization of the data set fitting a second-order polynomial. \n",
     "\n",
@@ -5029,9 +4712,7 @@
   {
    "cell_type": "markdown",
    "id": "fda06579",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "MSE(\\boldsymbol{y},\\boldsymbol{\\tilde{y}}) = \\frac{1}{n}\n",
@@ -5042,9 +4723,7 @@
   {
    "cell_type": "markdown",
    "id": "b0b67264",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and the $R^2$ score function.\n",
     "If $\\tilde{\\boldsymbol{y}}_i$ is the predicted value of the $i-th$ sample and $y_i$ is the corresponding true value, then the score $R^2$ is defined as"
@@ -5053,9 +4732,7 @@
   {
    "cell_type": "markdown",
    "id": "8634905c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "R^2(\\boldsymbol{y}, \\tilde{\\boldsymbol{y}}) = 1 - \\frac{\\sum_{i=0}^{n - 1} (y_i - \\tilde{y}_i)^2}{\\sum_{i=0}^{n - 1} (y_i - \\bar{y})^2},\n",
@@ -5065,9 +4742,7 @@
   {
    "cell_type": "markdown",
    "id": "b3d64229",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where we have defined the mean value  of $\\boldsymbol{y}$ as"
    ]
@@ -5075,9 +4750,7 @@
   {
    "cell_type": "markdown",
    "id": "a2f16298",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\bar{y} =  \\frac{1}{n} \\sum_{i=0}^{n - 1} y_i.\n",
@@ -5087,9 +4760,7 @@
   {
    "cell_type": "markdown",
    "id": "7e006fdd",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "You can use the functionality included in scikit-learn. If you feel for it, you can use your own program and define functions which compute the above two functions. \n",
     "Discuss the meaning of these results. Try also to vary the coefficient in front of the added stochastic noise term and discuss the quality of the fits.\n",
@@ -5103,10 +4774,7 @@
    "cell_type": "code",
    "execution_count": 49,
    "id": "7618a40a",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
@@ -5154,9 +4822,7 @@
   {
    "cell_type": "markdown",
    "id": "520f4a70",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "<!-- --- end solution of exercise --- -->"
    ]
@@ -5164,9 +4830,7 @@
   {
    "cell_type": "markdown",
    "id": "57017553",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Exercise 3: Normalizing our data\n",
     "\n",
@@ -5210,10 +4874,7 @@
    "cell_type": "code",
    "execution_count": 50,
    "id": "17d66f2c",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# split in training and test data\n",
@@ -5223,9 +4884,7 @@
   {
    "cell_type": "markdown",
    "id": "75e06a64",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Then we can use the standard scaler to scale our data as"
    ]
@@ -5234,10 +4893,7 @@
    "cell_type": "code",
    "execution_count": 51,
    "id": "789e6a0f",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "scaler = StandardScaler()\n",
@@ -5249,9 +4905,7 @@
   {
    "cell_type": "markdown",
    "id": "f02d2e90",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In this exercise we want you to to compute the MSE for the training\n",
     "data and the test data as function of the complexity of a polynomial,\n",
@@ -5267,10 +4921,7 @@
    "cell_type": "code",
    "execution_count": 52,
    "id": "48cb1804",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.random.seed()\n",
@@ -5284,9 +4935,7 @@
   {
    "cell_type": "markdown",
    "id": "bfffe7b0",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where $y$ is the function we want to fit with a given polynomial."
    ]
@@ -5294,9 +4943,7 @@
   {
    "cell_type": "markdown",
    "id": "0491f9ef",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "**a)**\n",
     "Write a first code which sets up a design matrix $X$ defined by a fifth-order polynomial.  Scale your data and split it in training and test data."
@@ -5305,9 +4952,7 @@
   {
    "cell_type": "markdown",
    "id": "e0a2615d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "**b)**\n",
     "Perform an ordinary least squares and compute the means squared error and the $R2$ factor for the training data and the test data, with and without scaling."
@@ -5316,16 +4961,32 @@
   {
    "cell_type": "markdown",
    "id": "5cc2a8aa",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "**c)**\n",
     "Add now a model which allows you to make polynomials up to degree $15$.  Perform a standard OLS fitting of the training data and compute the MSE and $R2$ for the training and test data and plot both test and training data MSE and $R2$ as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)?"
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }